Just What the Doctor Ordered. dT/dt
Is dT/dt a good way to kill the notion of AGW?
OK, so I’ve gone and built a new hammer… While this started off an an exploration of the “constant sample” technique of statistics (where each data item is compared to the prior time period, so you would always sample “this year vs last”) it has evolved into something more along the lines of a sloppy first derivative. Delta Temperature per Delta Time. Or dT/dt.
For each thermometer, and for each month, I simply find the degree to which the present reading is different from the last reading. How much did the temperature change during that time period?
That’s what we all want to know, after all. Isn’t it? IS the temperature changing, or not? So why not just go calculate that rather than going through all this other junk first?
Seemed reasonable to me, so I did.
What Have I Done?
Well, the first step simply looks at each individual thermometer record. Once a valid data item is found (for any given month) each subsequent valid data item is turned into a “change of Temp” relative to that first one. By definition, the first one has a “delta T” of zero. So if we measured 12.2 C in June of 1882, then in June of 1883 it was 12.4 C we would note that the temperature change was 0.2 C (what is often called an ‘anomaly’ relative to a ‘baseline’, though in this case the baseline is just the first valid reading for a thermometer in a month). If June 1884 comes up 12.6, we would get another 0.2 C of “delta T”. (Since the temperature changed by 0.2 C again relative to the last time we saw it).
This differs from the “constant sample” method in that if there are 5 missing years of data, I just go ahead and find the “delta T” over those whole 5 years. (NOT the average, the actual change, so if it was changing 0.2 C / year, I’d find 1889 was 1 C hotter than 1884 at 13.6 and show the 1889 “dT” as 1.0C). The years between would be shown as zeros. (Yes, I could do all sorts of fancy things to interpolate and fill in the missing values, and approximate that 0.2 C dT/dt for each missing value; and keep that 1.0 C from showing up in 1889. But WHY? All you really know is it popped up on the thermometer that year. The rest is guessing and imagining. I’d rather depend on just being aware of it and on averaging with other thermometers to smooth it out in large batches). In smaller (like single thermometer) batches it lets you see how dirty the data really are…
Kiribati, a Test Case
This code is still a bit “young” (it needs the comments cleaned up and some dead code from ‘bright ideas’ pruned) so it will be a couple of days until I post it. It is pretty simple, though. For now we’ll look at some test cases. These will start as ‘tabular reports’, but over time I’ll add graphs (now that I’m learning Open Office). It’s pretty easy to see what’s going on, anyway.
The tables will truncate on the right. This wordpress “Theme” does not put scroll bars on the bottom of preformated tables. The only one I’ve seen that does is the one used by WUWT and I’d like to keep something that looked more like “me” than “copy cat”, but if I can’t find another theme I’ll eventually just give up and become a ‘look alike’ (and HOPE that changing themes doesn’t damage all prior postings…?. that it’s a ‘swap at will’ thing). So you may find that you can’t see the far right of the tables.
Don’t worry, it’s not that interesting. I’ve put all the ‘good bits’ on the left edge. Their are just 12 monthly columns of the MONTHLY dT/dt averages for the thermometers in the given report group. And while it’s interesting to me to note that, oh, August has low dT/dt while January has more (typically) and it does let me do debugging / QA / validation: For most folks those 12 columns are not “the money quote”.
So what is?
We start with the year. I’ve chosen to “start time” in 1880 as does GIStemp. While it’s interesting to note that, for example, Sweden was as warm in 1720 as it is now, there are too few thermometers to defend against the charge of it just being a local effect or a sloppy very old thermometer. So we ‘go with the flow’ and start in 1880. If you see a report that starts in 1890 or even 1944, you know that there is no thermometer data for that report prior to those dates. Similarly, any that end prior to 2010 have had their thermometers dropped from GHCN.
A note on Provenance. I did a new 8 Feb 2010 download of GHCN data for this series. We can now start to see just how warm 2009 really wasn’t…
The next column is the running total dT over time. This is what most folks will care about. If it rises, say to 2 C, you can say we ‘warmed’ by 2 C at that location from the first thermometer record to the last. (But you really ought to see how good those records were…). You can also see if there is any “step function” in the warming.
I’ve seen a LOT of ‘step function’ where things are fine, then POP, we move up and stabilize again. These are ‘dig here” points for equipment changes, land use changes, etc.
Then we get the dT/dt that shows how much did this batch of thermometers, on average, change in this time period? (A single year, unless prior values were missing and full of zeros so we’re doing a ‘catch up’.) If there is acceleration and a ‘tipping point’ we ought to see these getting bigger as time passes.
There is a count of active thermometers in that year, so you can see as thermometers come and go from the record. With this approach (each measured against it’s own start datum) it is a helpful number, but not the focus of the report.
And finally the 12 columns of the monthly dT/dt values. These let you see any monthly patterns.
So, with that intro, here is Kiribati:
Well, all data on one chart is a bit much.
I’ll put a reduced chart below the table of data. Just dT and dT/dt by year.
Produced from input file: ./DTemps/Temps.504 Thermometer Records, Average of Monthly dT/dt, Yearly running total by Year Across Month, with a count of thermometer records in that year ----------------------------------------------------------------------------------- YEAR dT dT/yr Count JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC ----------------------------------------------------------------------------------- 1891 0.000 0.00 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1892 -1.067 -1.07 1 -1.0 -0.9 -1.1 -1.3 -1.3 -1.0 -0.8 -1.0 -0.8 -1.2 -1.5 -0.9 1893 -1.425 -0.36 1 -1.0 -0.9 -1.1 -0.4 -0.9 0.0 0.0 -0.3 0.2 -0.5 0.4 0.2 1894 -1.325 0.10 1 0.2 0.2 0.3 -0.3 0.1 0.2 0.5 0.0 0.0 0.0 0.0 0.0 1895 -0.342 0.98 1 1.1 0.8 1.1 1.3 1.2 0.4 0.0 0.9 0.7 1.7 1.1 1.5 1896 0.292 0.63 1 0.4 0.8 0.2 -0.4 0.2 0.5 1.0 1.2 0.8 1.1 1.3 0.5 1897 0.033 -0.26 1 0.0 0.0 0.0 0.0 0.0 0.0 -1.2 -0.8 0.2 -0.5 -0.2 -0.6 1898 -0.258 -0.29 1 -0.1 -0.8 0.0 0.1 0.3 0.0 0.8 -0.2 -0.6 -0.7 -1.3 -1.0 1899 -0.433 -0.17 1 -0.8 -0.2 -0.1 -0.4 -0.4 -0.2 0.0 0.0 0.0 0.0 0.0 0.0 1900 -0.333 0.10 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.8 1901 -0.083 0.25 1 0.8 0.7 0.7 1.4 0.0 -0.3 -0.1 -0.1 0.0 0.2 0.4 -0.7 1902 0.017 0.10 1 -0.5 -0.6 -1.2 -1.3 0.1 0.6 0.6 0.8 0.9 0.4 -0.4 1.8 1903 0.000 -0.02 1 -0.6 0.8 1.6 1.1 -0.1 -0.2 -0.6 0.0 -1.3 -0.9 0.0 0.0 1904 -0.283 -0.28 1 0.0 0.0 -1.1 -0.8 -0.5 -0.5 -0.5 -0.4 0.3 0.7 0.4 -1.0 1905 0.075 0.36 1 -0.3 -0.4 -1.4 0.9 0.6 0.4 0.9 0.3 1.0 0.8 0.5 1.0 1906 0.050 -0.03 1 2.4 -0.4 2.3 0.0 0.2 0.6 -0.3 -0.8 -0.4 -1.2 -1.3 -1.4 1907 -0.158 -0.21 1 -1.4 -0.2 -1.2 -0.1 -0.3 -0.7 -0.2 0.6 -0.4 0.6 0.4 0.4 1908 -0.625 -0.47 1 -1.2 0.3 0.0 -0.5 -0.3 -0.3 0.3 -0.1 -0.7 -1.3 -0.5 -1.3 1909 -1.025 -0.40 1 0.4 -0.7 -0.3 -0.3 -0.5 -0.2 -0.5 -0.7 0.2 -0.3 -1.3 -0.6 1910 -1.275 -0.25 1 -0.4 0.2 -0.7 -1.1 -0.4 -0.1 -0.7 -0.3 -0.4 0.0 0.6 0.3 1911 -0.525 0.75 1 0.2 0.0 -0.2 0.5 1.2 0.5 0.7 1.0 1.7 0.9 1.5 1.0 1912 -0.333 0.19 1 0.8 -0.4 2.2 0.6 0.4 0.8 -0.3 0.0 -1.0 -0.1 -0.8 0.1 1913 -0.542 -0.21 1 -0.1 0.3 -1.2 -0.1 -1.0 -1.4 0.6 0.0 0.0 0.0 0.0 0.4 1914 -0.333 0.21 1 -0.9 -0.2 -1.1 0.2 -0.2 0.7 0.5 0.8 0.8 -0.2 1.0 1.1 1915 -0.283 0.05 1 0.7 0.8 0.2 0.1 1.9 0.8 -0.2 -0.9 -0.7 0.3 -1.1 -1.3 1916 -1.075 -0.79 1 0.0 -0.4 0.5 -0.7 0.0 -1.0 -0.8 -0.8 -1.3 -1.3 -1.4 -2.3 1917 -1.750 -0.67 1 -1.4 -1.0 -1.1 -1.3 -2.5 -1.1 -0.4 -0.3 -0.2 0.3 0.1 0.8 1918 -0.525 1.23 1 0.7 1.1 0.6 1.3 0.6 1.4 0.7 0.7 1.9 0.6 2.4 2.7 1919 -0.217 0.31 1 1.5 0.3 1.3 0.2 0.8 -0.2 0.2 0.4 -0.9 0.1 0.0 0.0 1922 -0.217 0.00 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1923 0.442 0.66 1 0.0 1.4 0.6 0.7 0.1 0.3 -0.3 0.8 0.7 1.4 0.8 1.4 1924 0.317 -0.12 1 0.2 -0.9 -0.5 -0.3 0.3 0.1 0.6 -0.5 -0.5 0.0 0.0 0.0 1925 0.300 -0.02 1 0.6 -0.2 0.1 -0.2 -0.5 0.0 -0.6 0.6 0.8 -0.4 0.1 -0.5 1929 -0.017 -0.32 1 0.0 0.0 -0.5 -0.1 -0.3 -0.6 -0.3 -0.5 -0.7 -0.1 -0.9 0.2 1930 0.308 0.33 1 -0.1 0.5 0.9 0.0 0.4 1.0 0.9 0.0 -0.1 0.0 0.8 -0.4 1937 0.308 0.00 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1938 0.183 -0.12 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.1 -0.4 1939 0.767 0.58 1 -0.5 0.0 0.6 0.3 0.5 0.9 0.8 0.4 0.1 0.5 2.0 1.4 1940 1.167 0.40 1 1.9 1.2 0.8 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1942 0.933 -0.23 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.2 -0.8 -1.0 -0.8 1943 0.550 -0.38 1 -1.5 -1.2 -1.4 -1.1 0.2 -0.2 0.2 0.0 0.3 0.4 -0.2 -0.1 1944 1.292 0.74 1 0.1 0.1 1.0 1.1 0.7 1.1 1.0 1.6 0.8 0.7 0.7 0.0 1945 1.258 -0.03 1 0.2 0.2 -0.4 -0.5 -0.5 -0.6 -0.7 -0.2 0.2 0.2 0.4 1.3 1946 1.300 0.04 1 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1947 1.883 0.58 1 1.0 1.6 1.9 1.3 0.9 0.8 0.7 0.0 -0.3 -0.4 -0.1 -0.4 1948 1.817 -0.07 1 -0.8 -0.9 -0.9 -0.8 0.3 0.5 0.5 0.2 0.3 0.6 0.2 0.0 1949 1.742 -0.08 1 1.0 1.0 0.1 0.0 -0.5 -0.6 -0.2 -0.1 -0.1 -0.6 -0.5 -0.4 1950 1.275 -0.47 2 -1.1 -0.7 -0.5 -0.3 -0.3 -0.7 -0.7 -0.6 -0.6 0.1 -0.1 -0.1 1951 1.325 0.05 3 -0.1 0.0 0.0 0.2 0.0 -0.1 0.1 0.0 0.4 0.0 0.2 -0.1 1952 1.233 -0.09 3 0.4 0.2 -0.1 -0.1 0.0 0.1 0.0 0.1 -0.5 -0.2 -0.6 -0.4 1953 0.992 -0.24 3 -0.6 -0.7 -0.3 -0.7 -0.5 -0.1 0.1 -0.1 0.0 -0.1 0.2 -0.1 1954 0.825 -0.17 3 -0.1 -0.2 0.0 0.0 0.0 0.0 -0.8 -0.4 0.0 -0.3 -0.5 0.3 1955 0.450 -0.38 3 0.0 -0.1 -0.1 -0.2 -0.1 -0.5 0.0 -0.2 -0.6 -1.0 -0.7 -1.0 1956 0.567 0.12 4 -0.2 0.0 -0.1 0.0 0.0 -0.3 -0.2 0.0 0.0 0.5 0.7 1.0 1957 0.858 0.29 4 0.7 0.0 0.0 0.5 0.4 0.8 0.5 0.4 0.3 0.2 0.0 -0.3 1958 1.033 0.18 4 -0.2 0.6 0.2 0.1 0.2 0.2 0.2 0.3 0.3 0.2 0.1 -0.1 1959 0.975 -0.06 3 0.3 -0.3 0.3 0.0 -0.1 -0.2 -0.3 -0.7 -0.1 -0.2 0.0 0.6 1960 0.917 -0.06 3 -0.1 0.0 -0.3 -0.3 -0.2 -0.3 0.1 0.3 -0.2 -0.2 0.5 0.0 1961 0.717 -0.20 3 0.1 0.2 -0.3 0.0 -0.3 -0.1 -0.4 -0.2 -0.2 -0.2 -0.7 -0.3 1962 0.617 -0.10 3 -0.2 -0.1 0.0 -0.4 0.2 0.0 0.0 -0.2 -0.1 0.1 -0.1 -0.4 1963 0.742 0.12 4 -0.2 -0.1 0.0 0.2 0.2 0.1 0.1 0.4 0.3 0.0 0.2 0.3 1964 0.492 -0.25 3 0.0 0.2 0.2 0.1 -0.4 -0.3 -0.1 -0.7 -0.4 -0.4 -0.7 -0.5 1965 0.475 -0.02 3 -0.2 -0.4 -0.7 -0.7 0.2 0.1 0.0 0.0 0.2 0.1 0.3 0.9 1966 0.708 0.23 3 -0.1 0.2 0.6 0.6 0.2 0.5 0.3 0.9 0.1 0.2 -0.5 -0.2 1967 0.558 -0.15 4 0.1 -0.4 -0.1 -0.1 0.0 -0.3 -0.2 -0.5 -0.1 -0.1 0.1 -0.2 1968 0.658 0.10 3 0.2 0.2 0.0 -0.1 -0.5 -0.2 0.0 0.1 0.1 0.2 0.6 0.6 1969 0.875 0.22 4 0.1 0.3 0.2 0.4 0.5 0.3 0.1 0.1 0.0 0.3 0.7 -0.4 1970 0.400 -0.47 4 0.0 0.0 -0.4 -0.4 -0.3 0.0 -0.3 -0.4 -0.4 -1.0 -1.5 -1.0 1971 0.108 -0.29 4 -0.8 -0.7 -0.5 -0.6 -0.4 -0.4 -0.3 0.0 0.0 0.0 0.0 0.2 1972 0.558 0.45 4 0.2 0.7 0.4 0.5 0.6 0.4 0.3 0.1 0.0 0.5 0.9 0.8 1973 0.433 -0.12 4 0.5 0.1 0.5 0.2 -0.1 0.0 -0.2 0.1 0.0 -0.7 -0.8 -1.1 1974 0.425 -0.01 4 -0.6 -0.5 -0.6 -0.2 -0.2 0.0 0.2 0.0 0.3 0.7 0.3 0.5 1975 0.208 -0.22 4 0.4 0.2 -0.4 -0.2 -0.1 -0.3 0.0 -0.3 -0.4 -0.5 -0.5 -0.5 1976 0.358 0.15 4 -0.3 -0.2 0.3 -0.1 0.0 -0.3 -0.2 0.0 0.2 0.7 0.9 0.8 1977 0.825 0.47 4 0.0 0.0 0.0 0.5 0.2 0.9 0.9 1.3 0.5 0.7 0.3 0.3 1978 0.833 0.01 4 0.7 0.6 0.3 0.1 0.3 0.1 -0.2 -0.5 -0.1 -0.7 -0.2 -0.3 1979 0.925 0.09 4 -0.2 -0.3 -0.2 0.1 -0.2 0.0 0.1 0.2 0.5 0.5 0.5 0.1 1980 1.058 0.13 4 0.4 0.5 0.3 0.1 0.2 0.3 0.1 0.0 -0.3 0.0 -0.3 0.3 1981 0.992 -0.07 2 0.2 -0.2 -0.1 -0.2 -0.2 -0.3 0.1 -0.2 0.1 0.0 0.0 0.0 1982 0.958 -0.03 1 0.3 -0.1 1.0 0.9 0.4 0.7 -1.3 -0.7 -0.8 -0.6 -0.3 0.1 1983 0.958 0.00 1 -0.1 0.0 -0.6 0.0 -0.9 -0.7 1.1 0.7 0.4 -0.2 0.2 0.1 1984 0.425 -0.53 1 -0.4 -0.7 -0.6 -1.0 -0.2 -0.6 -1.2 -0.9 -0.1 0.2 0.1 -1.0 1985 0.700 0.27 1 0.2 0.3 -0.2 0.1 0.4 0.5 0.5 0.3 -0.2 0.3 0.2 0.9 1986 0.825 0.12 1 0.0 0.1 0.4 0.1 0.2 0.7 0.1 0.7 -0.2 0.0 -0.3 -0.3 1987 0.833 0.01 1 0.0 -0.2 -0.1 0.0 0.0 -0.3 -0.3 -0.3 0.3 0.0 0.5 0.5 1988 0.592 -0.24 1 0.4 0.4 0.4 0.0 -0.4 0.0 0.5 0.0 -0.5 -1.0 -1.5 -1.2 1989 0.442 -0.15 1 -1.1 -1.1 -0.9 -0.4 -0.1 -0.2 0.6 -0.1 0.1 0.9 0.0 0.5 1990 0.883 0.44 1 0.3 1.2 0.5 0.3 0.9 0.3 -0.2 0.3 0.2 0.5 0.7 0.3 1991 0.958 0.08 1 0.1 0.6 0.0 1.0 0.2 -0.1 0.0 -0.5 0.1 0.0 -0.4 -0.1 1992 0.725 -0.23 1 0.0 -0.8 -0.2 -1.2 -0.6 0.1 0.2 0.4 0.2 -0.6 0.2 -0.5 1993 0.617 -0.11 1 -0.2 -0.3 0.0 0.0 -0.4 0.0 0.0 -0.5 -0.4 0.0 0.3 0.2 1994 0.767 0.15 1 0.5 0.3 0.1 0.6 0.8 -0.2 0.0 0.3 0.0 -0.6 0.0 0.0 1995 0.842 0.07 1 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 1996 0.850 0.01 1 0.2 0.0 0.1 -0.7 -0.3 0.0 0.0 0.8 0.3 0.0 -0.3 0.0 1997 0.825 -0.03 1 0.0 0.3 0.0 0.6 0.0 -0.1 -0.3 -0.8 -0.1 -0.4 0.0 0.5 1998 0.875 0.05 1 0.0 0.3 0.0 0.0 0.0 0.7 0.5 0.0 0.0 -0.3 -0.6 0.0 For Country Code 504 From input file ./data/v2.mean.inv.1880.dt
Kiribati dT and dT/dt by year
Not a whole lot going on in Kiribati. It got a cold start, rapidly jumped up about 1 C in 1940, and then has more or less stayed the same since with some ripples. The data ‘cut off’ in 1998 for reasons only NOAA / NCDC can explain.
Weather Underground can find them just fine.
I like Kiribati as a test case since there are few enough thermometers to make hand processing for validation possible, yet enough to be interesting, and with some ‘edge cases’ too (like data cut off early, thermometer dropouts, etc.).
Larger areas? How about the entire Pacific?
OK, the ‘5’ region is the entire Pacific basin. Australia, New Zealand, Pacific Islands (ex-Hawaii that’s lumped in with the USA and Japan that is in with Asia), Indonesia, Philippines, etc. How does this tool look when used on larger areas?
Well, from 1880 to 1995 we warmed the entire 1/3 or so of the planet that is the Pacific Basin by… nothing. Though by 2006 we’d gotten up by 0.15 C and in 2009 it was up a whopping 0.46 C. Inside normal year to year variations such as 1942 and 1926. (Though in all honesty, my favorite is 1997 at MINUS 0.042, but I don’t want to be accused of a ‘cherry pick’ ;-)
Produced from input file: ./DTemps/Temps.5 Thermometer Records, Average of Monthly dT/dt, Yearly running total by Year Across Month, with a count of thermometer records in that year ----------------------------------------------------------------------------------- YEAR dT dT/yr Count JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC ----------------------------------------------------------------------------------- 1880 0.000 0.00 33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1881 -0.075 -0.07 36 -0.4 -0.4 -0.2 0.0 0.8 0.0 0.3 -0.4 -0.4 0.1 -0.3 0.0 1882 0.158 0.23 36 0.9 0.0 0.6 0.0 -0.5 0.2 -0.1 0.0 0.5 0.7 0.7 -0.2 1883 -0.067 -0.23 38 -0.5 -0.2 -0.7 0.0 -0.4 1.3 0.2 0.1 -1.1 -0.6 -0.7 -0.1 1884 -0.217 -0.15 38 -1.0 0.0 0.1 -0.1 -0.2 -0.6 0.0 0.3 0.7 0.1 0.0 -1.1 1885 -0.008 0.21 41 0.4 -0.3 -0.7 -0.5 0.5 -0.5 0.0 -0.2 0.3 1.3 0.3 1.9 1886 -0.058 -0.05 41 0.9 -0.1 0.2 0.7 -0.4 0.2 0.5 -0.2 0.0 -1.8 0.3 -0.9 1887 -0.133 -0.08 44 0.5 0.3 0.7 0.0 -0.4 -0.3 -0.1 0.0 -1.1 0.4 -1.1 0.2 1888 0.042 0.17 47 -0.9 -0.1 -1.2 -0.1 0.2 0.9 0.0 -0.2 1.0 0.1 1.8 0.6 1889 0.375 0.33 49 0.5 1.0 1.7 0.4 1.1 0.0 0.0 0.3 -0.4 0.5 -0.8 -0.3 1890 -0.075 -0.45 49 0.0 -0.6 -0.4 -0.5 -0.5 0.4 -0.5 -0.2 0.2 -0.8 -1.2 -1.3 1891 -0.383 -0.31 51 -1.5 -1.1 -0.2 -0.6 0.0 -0.8 0.2 -0.2 -0.5 0.0 0.6 0.4 1892 -0.242 0.14 52 0.4 1.4 0.9 -0.3 -0.5 -0.1 0.1 0.5 0.0 -0.1 0.2 -0.8 1893 -0.242 0.00 53 -0.2 -0.5 -1.0 0.2 0.3 -0.3 0.0 0.0 0.1 0.8 -0.2 0.8 1894 -0.400 -0.16 58 0.6 -0.5 0.0 0.3 -1.0 0.3 -0.5 -0.4 -0.9 -0.3 0.5 0.0 1895 -0.267 0.13 61 -0.5 0.2 -0.2 0.0 0.4 0.0 -0.6 0.5 0.9 0.8 -0.4 0.5 1896 -0.233 0.03 60 1.6 0.3 0.1 -0.2 0.0 -0.7 0.2 -1.2 -0.4 0.1 0.2 0.4 1897 -0.175 0.06 64 -1.3 -0.1 -0.8 0.8 -0.1 1.1 1.3 0.2 0.6 -1.5 0.5 0.0 1898 0.000 0.17 72 1.0 0.9 1.0 -0.7 -0.3 -0.5 -0.4 1.0 0.2 1.0 -0.7 -0.4 1899 -0.292 -0.29 75 -1.8 -0.1 0.2 0.5 0.2 -0.1 -0.7 -0.8 0.0 -1.2 -0.1 0.4 1900 -0.350 -0.06 80 1.4 0.1 -0.9 -1.1 0.0 0.5 0.0 -0.3 -1.4 0.9 0.6 -0.5 1901 -0.192 0.16 86 -0.6 -0.4 0.2 0.7 0.9 -1.2 -0.1 0.2 1.5 -0.2 0.8 0.1 1902 -0.292 -0.10 89 -0.1 -1.1 0.2 0.4 -0.1 0.7 0.7 0.0 -0.7 0.1 -0.3 -1.0 1903 -0.442 -0.15 101 0.4 0.4 0.1 -0.7 -0.5 0.0 -0.2 0.0 -0.2 -0.1 -0.9 -0.1 1904 -0.400 0.04 101 -0.4 -0.4 -0.8 1.1 0.4 0.0 0.1 0.1 -0.5 -0.2 0.0 1.1 1905 -0.567 -0.17 106 0.8 0.0 0.4 -0.2 0.3 0.1 -0.1 -0.4 -0.9 -1.4 -0.4 -0.2 1906 -0.042 0.53 109 0.7 1.8 -0.1 0.3 0.1 1.0 0.5 0.3 1.0 1.5 -0.8 0.0 1907 -0.183 -0.14 186 -0.8 -0.6 -0.2 -0.7 -0.1 -0.4 0.0 0.4 0.5 0.0 0.6 -0.4 1908 -0.317 -0.13 192 1.3 0.0 0.0 0.3 -0.3 -1.6 -0.3 -0.7 -1.0 -0.7 0.8 0.6 1909 -0.458 -0.14 199 -1.7 -0.6 0.4 -1.1 -0.1 1.7 -0.1 0.5 0.4 0.7 -0.7 -1.1 1910 -0.042 0.42 210 0.5 0.9 0.0 1.5 0.8 0.1 0.7 0.7 1.3 -1.1 -0.4 0.0 1911 -0.258 -0.22 215 -0.5 -0.9 -0.2 -1.1 -0.5 -1.3 -0.1 -0.3 -0.5 0.8 1.3 0.7 1912 0.150 0.41 219 0.9 1.9 1.1 0.5 0.0 1.4 0.1 0.0 -0.3 0.1 -1.0 0.2 1913 -0.142 -0.29 234 -0.3 -0.8 -1.2 0.5 -0.8 -0.9 0.2 -0.6 -0.2 0.4 -0.2 0.4 1914 0.642 0.78 239 0.6 0.6 1.4 0.2 1.4 0.8 -0.6 1.0 0.7 0.8 2.1 0.4 1915 0.400 -0.24 243 -0.5 0.5 -0.1 0.0 -0.6 0.2 1.5 -0.5 0.4 -1.3 -1.5 -1.0 1916 -0.108 -0.51 243 0.4 -0.8 -0.2 -0.9 0.5 -0.2 -0.9 -0.2 -0.4 -0.5 -2.2 -0.7 1917 -0.592 -0.48 245 -0.6 -2.2 -1.0 -0.7 -1.5 -0.6 0.2 -0.1 -0.5 0.2 0.7 0.3 1918 -0.242 0.35 245 -0.3 0.8 0.0 0.7 1.3 0.5 -1.1 0.5 0.0 0.0 1.3 0.5 1919 0.392 0.63 247 0.8 1.5 0.7 1.2 0.8 0.4 0.6 -0.5 0.1 0.6 0.8 0.6 1920 -0.117 -0.51 244 -0.8 -0.6 -0.5 -1.2 -1.3 -0.2 0.4 -0.1 -0.2 -0.1 -0.6 -0.9 1921 0.233 0.35 254 0.4 0.4 0.3 0.3 1.6 0.9 0.9 -0.3 0.4 -0.8 0.2 -0.1 1922 0.017 -0.22 253 -0.5 -0.3 0.0 1.0 -1.0 -1.0 -1.5 0.0 -0.5 1.1 0.0 0.1 1923 0.050 0.03 251 0.3 0.7 0.9 -0.3 0.9 0.0 0.1 0.0 -0.4 -0.8 -1.5 0.5 1924 -0.408 -0.46 253 -0.4 -1.6 -1.1 -1.8 -1.0 -0.4 0.7 0.8 0.8 0.1 0.2 -1.8 1925 -0.417 -0.01 259 -0.3 0.2 0.0 0.9 0.1 0.5 -1.2 -0.8 -1.5 0.0 0.6 1.4 1926 0.125 0.54 259 0.7 1.6 0.9 0.4 -0.6 0.0 1.3 0.8 1.6 0.6 0.0 -0.8 1927 -0.275 -0.40 260 0.2 -1.8 -0.7 -0.8 0.0 -0.4 -0.9 -0.6 -0.4 0.0 0.5 0.1 1928 0.175 0.45 255 -0.5 0.7 0.6 1.3 0.0 -0.2 0.6 1.5 1.2 -0.4 -0.3 0.9 1929 -0.450 -0.62 259 0.9 0.5 -0.6 -1.8 0.0 -0.1 -1.5 -1.3 -1.7 0.0 -0.8 -1.1 1930 0.017 0.47 262 -0.7 0.1 0.4 0.5 0.8 0.9 2.0 0.3 0.2 0.4 0.4 0.3 1931 -0.250 -0.27 268 0.0 -0.9 -0.2 -0.1 0.1 0.0 -0.8 0.0 0.2 -1.0 -0.7 0.2 1932 -0.117 0.13 272 1.7 0.0 0.1 0.3 0.0 -0.7 -0.3 -0.1 0.0 0.0 0.8 -0.2 1933 -0.142 -0.03 274 -1.6 0.0 0.6 0.1 -0.3 0.8 0.6 -0.7 0.0 1.0 -0.6 -0.2 1934 -0.125 0.02 275 0.3 0.0 0.1 -0.4 0.5 -0.6 0.2 0.8 0.3 -1.0 0.0 0.0 1935 -0.250 -0.13 278 -0.3 0.1 -1.0 -0.1 -1.2 -0.2 -0.5 0.3 -0.4 0.8 0.4 0.6 1936 -0.117 0.13 282 0.2 0.0 0.2 -0.2 0.6 -0.2 0.4 0.4 0.0 0.1 0.0 0.1 1937 -0.067 0.05 283 -0.4 -0.1 0.0 0.0 0.0 0.0 -0.5 -0.3 0.6 0.5 0.5 0.3 1938 0.283 0.35 299 0.4 0.0 0.8 1.5 1.1 0.6 0.0 -0.6 -0.1 0.1 0.3 0.1 1939 -0.100 -0.38 314 1.2 0.8 -0.7 -0.5 -0.2 0.1 -0.5 -0.3 -0.7 -1.5 -1.4 -0.9 1940 0.117 0.22 317 -0.8 -0.7 0.9 -0.5 -1.5 0.1 0.6 0.8 1.0 1.5 0.3 0.9 1941 -0.242 -0.36 321 -1.4 -0.4 -1.7 0.6 0.6 -0.2 0.0 -0.7 -0.2 -1.3 0.6 -0.2 1942 0.217 0.46 310 1.8 0.1 1.1 0.0 0.8 0.9 0.2 1.1 0.2 0.2 -0.5 -0.4 1943 -0.433 -0.65 314 -1.1 0.2 0.3 -0.8 -1.3 -1.7 -0.8 -1.6 -0.4 0.1 -0.6 -0.1 1944 -0.175 0.26 315 0.9 -0.3 -1.0 -0.5 -0.5 0.5 0.5 0.8 0.7 0.2 1.6 0.2 1945 -0.092 0.08 321 -0.5 0.1 -0.3 1.0 0.8 1.4 -0.4 0.8 -0.8 -0.6 -0.9 0.4 1946 -0.317 -0.22 332 0.4 0.2 -0.4 -0.9 0.3 -2.1 0.6 -0.7 0.0 -0.2 0.2 -0.1 1947 -0.167 0.15 341 0.2 0.4 1.0 0.5 0.6 1.3 -0.1 0.0 0.0 0.0 -1.3 -0.8 1948 -0.350 -0.18 349 -1.6 0.1 -0.7 -0.1 -1.2 -0.3 -0.4 0.3 0.2 0.4 0.6 0.5 1949 -0.508 -0.16 374 0.2 -1.0 0.2 -0.3 0.0 -0.8 0.3 -0.2 -0.2 0.2 -0.2 -0.1 1950 -0.258 0.25 391 0.3 0.1 0.1 0.6 0.7 0.7 0.5 -0.1 0.4 -0.4 0.0 0.1 1951 -0.333 -0.08 461 0.0 0.3 0.5 -0.6 -0.6 0.3 -0.6 -0.6 0.0 0.0 0.4 0.0 1952 -0.283 0.05 472 0.7 0.0 -0.3 0.5 0.3 -0.1 0.0 0.6 -0.3 0.0 -0.6 -0.2 1953 -0.308 -0.02 479 -0.9 -0.5 0.2 0.8 0.0 -0.3 0.1 -0.4 -0.1 0.0 0.3 0.5 1954 -0.275 0.03 481 0.4 0.1 -0.4 -0.3 0.0 0.0 0.3 0.7 0.0 0.0 -0.1 -0.3 1955 -0.317 -0.04 494 0.0 0.7 0.2 -0.1 -0.2 0.0 -0.4 0.0 0.3 0.0 -0.4 -0.6 1956 -0.583 -0.27 501 -0.3 0.0 0.0 -0.1 0.0 -0.3 0.1 -1.0 -0.9 -0.7 -0.1 0.1 1957 -0.233 0.35 405 0.1 -0.4 -0.3 0.3 0.1 1.3 -0.4 0.7 0.3 0.7 0.9 0.9 1958 -0.142 0.09 407 0.1 0.4 0.5 0.2 1.1 -0.6 0.6 0.1 -0.3 -0.3 -0.1 -0.6 1959 -0.100 0.04 416 0.3 -0.1 0.1 -0.1 -1.0 0.0 0.2 0.0 0.6 0.0 0.5 0.0 1960 -0.458 -0.36 448 0.0 -0.3 -0.4 -0.4 -0.7 -0.7 -0.3 -0.7 -0.2 0.3 -1.1 0.2 1961 -0.233 0.23 457 -0.1 0.3 0.1 0.4 0.5 0.3 -0.2 0.1 0.3 0.3 0.5 0.2 1962 -0.258 -0.03 505 0.0 -0.2 -0.2 -0.2 0.0 0.7 0.5 0.1 -0.2 -0.6 0.1 -0.3 1963 -0.325 -0.07 504 -0.4 0.0 0.1 -0.2 0.3 -0.7 -0.4 0.1 0.0 0.4 -0.2 0.2 1964 -0.367 -0.04 508 0.4 0.0 0.0 0.4 -0.3 0.2 0.4 0.1 0.1 -0.8 -0.2 -0.8 1965 -0.217 0.15 634 -0.4 0.4 -0.2 -0.4 0.1 0.0 -0.7 0.1 0.4 1.1 0.3 1.1 1966 -0.333 -0.12 658 0.5 -0.2 0.2 0.5 -0.4 -0.1 0.3 -0.2 -0.5 -0.9 0.0 -0.6 1967 -0.200 0.13 669 -0.2 0.0 -0.6 0.1 0.5 0.6 0.1 0.0 0.0 1.1 0.0 0.0 1968 -0.283 -0.08 676 0.3 0.2 0.8 0.1 -0.6 -0.5 -0.2 0.0 -0.2 -0.7 -0.2 0.0 1969 -0.108 0.17 697 0.4 0.0 -0.1 -0.3 0.6 0.0 0.7 0.8 -0.5 0.3 0.1 0.1 1970 -0.192 -0.08 709 -0.5 0.0 0.0 0.1 -0.3 0.4 -0.3 -0.6 0.3 -0.1 -0.2 0.2 1971 -0.292 -0.10 702 0.0 -0.1 0.2 -0.1 0.0 -0.7 -0.3 0.1 0.5 -0.1 -0.5 -0.2 1972 -0.083 0.21 713 -0.5 -0.2 -0.6 -0.1 0.4 0.3 0.2 0.2 0.4 0.3 0.9 1.2 1973 0.242 0.33 705 1.4 0.4 0.4 0.7 0.5 0.2 1.0 0.1 -0.1 0.1 -0.1 -0.7 1974 -0.258 -0.50 707 -0.8 -0.6 0.3 -0.4 -0.6 -0.4 -0.7 -0.4 -0.7 -0.7 -0.7 -0.3 1975 -0.117 0.14 710 -0.4 0.4 -0.6 -0.2 0.2 0.0 0.6 0.0 0.7 0.0 0.8 0.2 1976 -0.383 -0.27 619 -0.1 -0.3 0.2 0.0 -0.5 0.0 -0.8 -0.3 -0.8 -0.2 -0.5 0.1 1977 -0.067 0.32 619 0.7 0.6 0.0 0.0 0.0 -0.1 -0.2 0.8 0.0 1.2 0.7 0.1 1978 -0.158 -0.09 624 0.0 -0.1 0.7 0.3 0.6 0.1 0.0 -0.7 0.0 -0.8 -0.5 -0.7 1979 0.125 0.28 622 0.7 0.0 -0.2 -0.1 -0.8 0.7 0.2 0.1 0.5 0.4 0.8 1.1 1980 0.300 0.18 625 -0.6 0.0 0.0 0.5 1.2 -0.3 0.0 0.6 0.7 0.2 0.0 -0.2 1981 0.142 -0.16 617 0.6 0.3 -0.6 0.3 -0.5 -0.3 0.1 -0.5 0.1 -0.1 -1.1 -0.2 1982 -0.017 -0.16 585 -0.2 0.0 0.2 -0.9 -0.2 -0.7 -1.0 0.8 -1.1 -0.4 1.4 0.2 1983 0.133 0.15 593 -0.6 0.8 0.7 -0.5 0.5 0.9 0.3 -0.3 0.9 0.6 -1.1 -0.4 1984 -0.308 -0.44 590 -0.7 -1.4 -1.3 0.4 -0.4 0.1 0.0 -0.3 -1.4 -0.4 0.3 -0.2 1985 -0.042 0.27 581 0.8 0.5 1.3 0.3 0.1 -0.5 0.2 0.0 0.4 0.0 0.0 0.1 1986 -0.100 -0.06 551 -0.2 -0.3 0.0 0.1 0.1 0.2 0.0 -0.4 0.3 -0.2 -0.2 -0.1 1987 -0.000 0.10 456 0.1 -0.1 -1.0 0.1 -0.1 0.5 0.0 0.4 0.1 0.5 0.4 0.3 1988 0.383 0.38 430 0.8 0.0 0.8 0.0 0.6 0.0 0.8 0.3 0.5 1.0 -0.2 0.0 1989 -0.033 -0.42 427 -0.7 0.1 -0.1 -0.1 -0.1 -0.8 -0.9 -1.0 -0.5 -0.9 0.0 0.0 1990 0.175 0.21 523 0.2 -0.1 0.3 0.0 0.0 0.1 0.4 0.2 -0.1 0.1 0.6 0.8 1991 0.267 0.09 568 0.2 0.5 -0.4 -0.1 0.0 1.3 0.0 0.4 0.1 0.6 -0.7 -0.8 1992 -0.092 -0.36 530 -1.0 -0.5 0.3 0.2 -0.2 -1.2 0.2 -0.2 -0.7 -1.0 -0.2 0.0 1993 -0.017 0.07 126 0.2 -0.1 -0.3 0.0 0.0 0.0 0.1 0.1 0.1 0.0 0.6 0.2 1994 -0.000 0.02 123 0.2 0.2 0.0 -0.2 0.0 0.0 -0.5 -0.4 0.0 0.4 0.0 0.5 1995 -0.000 -0.00 124 -0.2 -0.2 0.0 -0.1 -0.1 0.2 0.0 0.5 0.3 0.0 0.1 -0.5 1996 0.017 0.02 132 -0.1 0.0 0.0 0.0 0.0 0.2 0.3 -0.2 0.0 0.1 -0.1 0.0 1997 -0.042 -0.06 132 -0.1 0.2 -0.2 0.0 -0.1 -0.5 -0.4 -0.2 0.0 0.0 0.2 0.4 1998 0.375 0.42 122 0.6 0.4 0.8 0.4 0.7 0.4 0.5 0.7 0.4 0.1 0.0 0.0 1999 0.108 -0.27 123 0.0 -0.4 -0.3 -0.5 -0.3 -0.2 0.0 -0.4 -0.2 -0.1 -0.4 -0.4 2000 0.058 -0.05 128 -0.5 0.0 -0.2 0.1 -0.5 -0.2 0.0 -0.1 0.2 -0.1 0.3 0.4 2001 0.125 0.07 125 0.5 0.0 0.0 0.2 0.1 0.4 0.0 0.2 0.0 -0.1 -0.2 -0.3 2002 0.275 0.15 125 -0.2 -0.2 0.2 0.2 0.4 0.1 0.1 0.0 -0.1 0.4 0.5 0.4 2003 0.200 -0.07 125 0.1 0.2 -0.1 -0.2 0.0 0.0 -0.1 0.0 -0.1 -0.5 -0.2 0.0 2004 0.183 -0.02 131 0.0 0.1 0.0 0.0 -0.3 -0.1 -0.1 -0.1 -0.2 0.6 0.0 -0.1 2005 0.350 0.17 138 0.1 0.0 0.2 0.3 0.3 0.0 0.3 0.1 0.3 0.0 0.1 0.3 2006 0.150 -0.20 140 0.1 -0.1 0.0 -0.8 -0.7 -0.6 -0.3 0.1 0.2 0.0 0.1 -0.4 2007 0.292 0.14 144 -0.2 0.3 0.0 0.4 1.0 0.1 0.0 0.1 0.0 0.1 -0.1 0.0 2008 0.225 -0.07 159 0.2 -0.4 0.0 -0.3 -0.5 0.4 0.0 -0.4 0.1 0.1 0.0 0.0 2009 0.467 0.24 160 0.0 0.3 0.0 0.3 -0.1 0.0 0.2 1.0 0.2 -0.2 0.8 0.4 2010 0.483 0.02 121 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 For Country Code 5 From input file ./data/v2.mean.inv.1880.dt
Maybe the report just doesn’t show warming trends? Could I have a structural issue in the code? Time to “blame Canada” again:
Blame Canada
We see that they get warm during the “cold ’60s and ’70s” then as thermometer dropouts take their toll, the temperatures really stack up.
I’ve looked into one area, Saskatchewan, and traced one cluster back to a station that has a very high reading. This was odd as the “Really Rural” stations (with “R” flag and not airports) had very high warming (5 to 6 C in one year) while the non-Rural and Airports did not. Looks like bad stations (perhaps near to heated rural buildings?) to me, but that will take a Canadian Surface Stations project to sort out. I’ll cover this case in a future posting. For now, we know the report finds heat when it is in the data. From whatever cause… (That jump about 2005 / 2006 in The Lesser Dying of Thermometers has a station in Sask. return after a 60+ year absence, and with a +18 C warming spike in January. I left it in, since it IS part of the data… but that regional group needs investigation. And I need to think about how to handle the return of prodigal thermometers… )
Produced from input file: ./DTemps/Temps.403 Thermometer Records, Average of Monthly dT/dt, Yearly running total by Year Across Month, with a count of thermometer records in that year ----------------------------------------------------------------------------------- YEAR dT dT/yr Count JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC ----------------------------------------------------------------------------------- 1880 0.000 0.00 57 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1881 0.283 0.28 63 -4.8 -1.0 3.4 0.0 0.0 -1.5 0.0 0.9 1.2 -0.2 1.4 4.0 1882 -0.067 -0.35 67 1.4 1.7 -1.8 -0.6 -2.8 0.5 -0.6 -0.1 -1.2 1.7 0.4 -2.8 1883 -1.158 -1.09 68 -2.4 -3.5 -2.9 0.4 0.1 0.8 -0.3 -1.1 -1.4 -2.3 0.2 -0.7 1884 -0.667 0.49 71 0.0 0.8 1.9 1.0 0.8 0.5 -0.9 0.5 1.3 0.8 -0.2 -0.6 1885 -0.783 -0.12 79 0.6 -2.2 -2.4 -0.5 0.2 -1.3 1.7 -1.1 -0.9 0.0 1.9 2.6 1886 -0.308 0.48 79 0.1 2.8 3.1 2.0 0.3 0.5 0.1 1.0 0.0 0.9 -1.7 -3.4 1887 -0.775 -0.47 78 -1.1 -2.3 -1.2 -2.4 1.7 0.5 0.9 -0.7 -0.1 -2.1 -0.5 1.7 1888 -0.775 -0.00 79 -0.5 1.6 -0.7 -0.8 -3.0 -0.1 -1.3 0.1 0.6 0.3 0.9 2.9 1889 0.817 1.59 82 6.8 -0.2 5.5 3.5 1.8 -0.3 0.4 0.6 0.5 0.2 1.0 -0.7 1890 -0.208 -1.02 90 -3.9 0.2 -3.7 -2.0 -2.0 0.3 0.0 -0.5 -0.6 1.0 0.4 -1.5 1891 0.483 0.69 89 3.7 -0.4 0.2 1.9 0.9 -0.6 -1.3 0.4 1.7 -0.1 -1.6 3.5 1892 0.158 -0.33 93 -1.8 2.3 1.0 -1.6 -0.8 0.2 1.1 0.2 -0.6 0.4 -0.5 -3.8 1893 -0.683 -0.84 101 -2.3 -3.2 -1.7 -2.1 0.9 0.7 -0.1 0.2 -1.2 -0.2 0.0 -1.1 1894 0.500 1.18 104 1.4 1.5 2.9 2.9 0.4 0.2 0.8 -0.2 0.7 0.3 -0.1 3.4 1895 0.258 -0.24 114 -0.2 0.9 -2.2 1.3 0.8 -0.3 -1.2 -0.6 -0.5 -1.4 0.6 -0.1 1896 0.033 -0.23 115 0.1 1.7 -0.8 -1.5 0.0 0.0 0.8 0.3 -0.3 0.4 -3.2 -0.2 1897 0.242 0.21 126 1.2 -1.1 -0.1 0.5 -0.2 -1.1 0.2 0.0 1.5 1.1 1.1 -0.6 1898 0.817 0.58 130 0.7 1.0 2.8 -0.1 0.3 1.0 -0.1 0.7 0.0 -1.0 1.3 0.3 1899 0.092 -0.72 134 -1.2 -3.8 -4.1 -0.4 -1.3 -0.6 -0.4 -0.9 -1.0 0.6 3.7 0.7 1900 0.917 0.82 136 2.6 1.7 1.7 2.2 1.2 0.9 -0.1 0.6 0.6 1.6 -3.3 0.2 1901 0.875 -0.04 132 -2.1 0.5 1.6 -0.8 0.5 -0.9 0.7 0.3 -0.5 -0.3 0.6 -0.1 1902 0.708 -0.17 138 1.5 2.6 1.8 -0.6 -1.1 -1.7 -0.7 -0.8 0.2 -0.9 0.7 -3.0 1903 0.358 -0.35 141 -0.7 -1.5 -1.4 -0.6 -0.3 1.6 -0.4 -1.0 -0.8 0.4 -0.9 1.4 1904 -0.292 -0.65 139 -2.0 -5.2 -2.8 -0.2 0.5 0.0 0.3 0.3 0.1 -0.4 2.5 -0.9 1905 0.533 0.83 142 -0.4 3.4 4.1 0.6 -0.7 -0.2 0.6 0.9 1.2 -1.4 -1.0 2.8 1906 0.958 0.42 140 4.1 2.0 -2.7 1.4 -0.1 0.9 0.8 0.6 0.5 1.9 -0.6 -3.7 1907 -0.467 -1.43 150 -6.5 -1.9 0.2 -4.7 -2.5 -0.6 -1.4 -1.9 -1.4 -0.7 0.8 3.5 1908 0.617 1.08 161 5.3 1.5 -1.0 2.1 3.0 0.3 1.0 0.5 1.6 0.2 0.2 -1.7 1909 0.017 -0.60 164 -3.6 -0.8 1.9 -1.8 -0.7 0.3 -0.7 0.8 -0.2 -0.3 -1.5 -0.6 1910 0.900 0.88 168 4.1 -1.1 3.8 4.4 0.1 0.0 0.5 -1.0 -1.2 0.8 0.0 0.2 1911 0.217 -0.68 175 -4.8 1.3 -3.1 -2.4 1.3 0.3 -0.5 0.3 -0.3 -0.5 -1.9 2.1 1912 0.317 0.10 189 -0.2 1.1 -2.5 0.5 -0.2 -0.2 -0.6 -0.6 0.1 0.1 3.7 0.0 1913 0.567 0.25 208 2.0 -2.1 0.6 1.2 -1.1 0.0 0.1 1.0 0.4 -0.7 0.3 1.3 1914 0.433 -0.13 228 1.7 -1.4 1.8 -1.6 1.1 -0.5 1.0 -0.2 0.1 2.0 -1.2 -4.4 1915 1.233 0.80 251 0.3 5.1 0.8 3.5 -0.5 -0.7 -1.3 0.8 -0.3 -0.8 0.1 2.6 1916 -0.150 -1.38 256 -4.2 -3.1 -3.2 -2.4 -0.6 0.3 1.7 -0.2 0.2 -1.3 -0.5 -3.3 1917 -0.633 -0.48 258 1.4 -2.5 1.2 -1.9 -0.5 -0.3 -0.3 -0.3 0.1 -1.2 2.2 -3.7 1918 0.375 1.01 274 0.4 1.6 1.1 1.6 0.5 0.3 -0.9 -0.1 -0.6 2.1 -0.6 6.7 1919 0.458 0.08 265 5.6 1.9 -1.8 0.2 1.2 2.2 0.9 0.6 1.0 -3.1 -4.0 -3.7 1920 0.567 0.11 271 -6.3 1.3 1.2 -3.1 -0.6 -1.5 -0.2 0.5 0.2 3.9 2.5 3.4 1921 1.133 0.57 276 4.0 0.4 0.3 3.0 1.0 1.5 1.2 -1.2 -0.4 -0.1 -2.1 -0.8 1922 0.575 -0.56 285 -1.4 -4.3 0.0 -0.4 0.5 -0.5 -1.5 0.8 0.6 -0.8 2.9 -2.6 1923 0.675 0.10 295 -0.5 0.5 -3.0 -1.3 -1.4 0.1 0.4 -1.3 -0.2 0.9 1.7 5.3 1924 0.317 -0.36 300 -0.4 3.5 4.5 0.0 -0.6 -1.6 -0.3 0.1 -0.6 0.5 -2.9 -6.5 1925 0.617 0.30 300 -0.6 -0.8 -0.7 2.2 0.7 0.9 0.0 0.9 -0.2 -4.5 0.2 5.5 1926 0.608 -0.01 312 4.4 1.3 -0.2 -1.7 0.2 -1.0 0.3 -0.8 -1.6 2.7 -1.0 -2.7 1927 0.158 -0.45 303 -2.6 -1.9 1.2 -0.1 -1.5 0.3 -0.2 0.2 2.2 1.1 -1.7 -2.4 1928 1.142 0.98 299 1.7 1.7 -1.0 -1.3 2.4 0.0 0.3 0.0 -0.8 -1.3 3.9 6.2 1929 0.200 -0.94 310 -4.4 -3.5 0.9 1.0 -2.0 0.2 -0.4 0.3 0.3 1.4 -0.9 -4.2 1930 1.167 0.97 318 -0.4 3.5 -0.8 1.7 0.7 1.0 0.5 0.9 0.4 -1.7 1.1 4.7 1931 2.192 1.02 321 6.3 2.8 0.4 0.5 0.5 -0.1 0.5 -0.5 0.5 2.2 0.0 -0.8 1932 0.658 -1.53 321 -1.4 -5.2 -3.1 -0.9 0.5 0.2 -1.3 0.6 -0.1 -2.2 -2.8 -2.7 1933 0.150 -0.51 323 -0.7 -0.2 1.9 -0.7 -0.5 -0.1 0.5 -0.1 -0.4 -0.5 0.0 -5.3 1934 0.892 0.74 334 0.4 0.7 -0.1 1.4 1.0 -0.9 0.0 -1.6 -0.7 1.5 3.0 4.2 1935 0.175 -0.72 338 -4.9 2.5 -1.1 -2.4 -2.1 -0.1 1.1 0.4 0.3 -0.9 -3.5 2.1 1936 0.042 -0.13 338 1.2 -9.3 2.9 -0.2 2.0 0.6 0.0 0.5 0.1 -0.1 1.6 -0.9 1937 0.858 0.82 344 -0.6 6.5 -0.9 2.2 0.0 0.8 -0.1 0.5 0.8 1.1 0.1 -0.6 1938 1.408 0.55 351 3.3 -1.1 1.2 -0.4 -0.8 -0.1 -0.1 -0.4 1.3 1.5 0.0 2.2 1939 0.892 -0.52 353 1.0 -1.9 -3.4 -0.2 0.3 -1.9 -0.1 0.6 -2.0 -3.2 1.9 2.7 1940 1.067 0.18 352 -1.5 4.4 2.5 -0.1 0.2 0.3 -0.3 -0.5 1.9 1.9 -3.6 -3.1 1941 1.392 0.33 357 0.1 0.6 0.0 2.3 0.0 1.4 1.0 -0.9 -2.6 -0.6 2.4 0.2 1942 1.267 -0.12 371 2.0 -0.6 2.1 -0.6 -0.1 -0.6 -1.3 0.6 0.8 1.1 -1.8 -3.1 1943 0.783 -0.48 383 -4.7 0.7 -5.0 -1.1 -1.5 -0.8 0.4 -0.3 0.1 0.2 2.8 3.4 1944 1.583 0.80 393 6.6 -1.5 0.9 0.6 2.4 0.8 -0.3 0.7 0.7 -0.2 -0.6 -0.5 1945 0.742 -0.84 394 -3.3 1.0 4.5 -1.6 -2.5 -1.0 -0.1 0.0 -1.4 -1.0 -2.9 -1.8 1946 0.900 0.16 392 0.0 -2.4 0.7 1.8 0.9 0.4 -0.1 -0.6 0.9 -0.1 0.5 -0.1 1947 1.000 0.10 395 -0.3 1.1 -2.8 -2.2 -0.9 -0.2 1.0 0.4 -0.7 2.3 1.4 2.1 1948 0.708 -0.29 409 0.7 -2.5 -3.2 -0.5 1.4 0.9 -0.6 0.1 1.3 -1.3 1.5 -1.3 1949 0.992 0.28 407 -0.9 0.0 2.7 3.3 -0.2 0.0 -0.1 0.3 -0.9 -0.6 -0.1 -0.1 1950 -0.167 -1.16 422 -4.9 1.2 -2.0 -3.3 -0.3 -0.4 -0.5 -1.6 -0.1 -0.5 -2.9 1.4 1951 0.333 0.50 441 3.8 1.5 0.2 2.4 1.1 -0.5 0.3 0.4 -0.1 -0.6 0.1 -2.6 1952 1.517 1.18 441 -0.5 1.4 1.2 1.3 -0.2 0.5 0.4 0.5 0.7 0.9 2.8 5.2 1953 1.858 0.34 449 1.9 1.3 1.1 -1.3 -0.4 -0.1 -0.4 0.4 -0.3 1.5 1.2 -0.8 1954 0.933 -0.92 450 -4.1 1.1 -1.3 -3.5 -0.9 0.0 -0.3 -0.8 -0.4 -0.8 -0.2 0.1 1955 0.583 -0.35 455 5.3 -4.1 -2.3 3.8 0.7 0.9 1.0 0.9 -0.2 0.3 -5.5 -5.0 1956 0.350 -0.23 458 -0.7 -0.3 0.7 -1.8 -1.4 -0.9 -1.2 -1.0 -0.8 -0.9 3.8 1.7 1957 0.833 0.48 476 -4.0 0.5 2.9 0.8 1.2 -0.2 0.4 -0.1 1.9 0.0 0.0 2.4 1958 1.392 0.56 478 7.1 0.0 1.0 1.0 0.8 -0.3 0.2 0.9 -0.5 0.5 -1.0 -3.0 1959 0.692 -0.70 482 -4.9 -1.4 -1.8 -0.9 -1.2 0.3 0.6 -0.6 0.0 -1.9 -0.9 4.3 1960 1.258 0.57 488 1.5 3.8 -2.3 0.0 1.5 0.4 -0.3 0.6 0.4 1.9 1.3 -2.0 1961 1.075 -0.18 491 -0.5 -1.3 1.8 -0.3 -1.2 0.7 0.1 0.6 -0.4 -0.4 0.1 -1.4 1962 0.883 -0.19 500 -0.4 -3.5 0.7 0.2 0.0 -0.6 -1.3 -0.9 -0.1 1.2 0.9 1.5 1963 1.125 0.24 503 -0.2 2.6 -1.1 0.8 0.0 -0.1 1.2 0.0 0.3 1.3 -0.2 -1.7 1964 0.592 -0.53 508 2.4 2.3 -2.1 -1.2 0.7 -0.6 -0.2 -1.1 -1.1 -2.3 -1.3 -1.9 1965 0.283 -0.31 521 -2.8 -4.1 1.4 0.2 -0.2 0.0 -0.8 0.7 -0.9 -0.1 -0.8 3.7 1966 0.683 0.40 536 -1.5 2.3 2.4 -0.7 -0.4 0.4 0.8 0.1 2.3 -0.4 -0.4 -0.1 1967 0.642 -0.04 542 3.1 -2.6 -3.8 -1.0 -0.7 0.3 0.0 0.8 0.6 0.6 1.8 0.4 1968 0.933 0.29 547 -1.6 2.1 4.8 2.6 0.9 -1.0 -0.5 -2.0 -0.2 0.9 0.1 -2.6 1969 1.042 0.11 564 -1.3 1.3 -2.6 0.0 0.0 0.1 -0.1 1.7 -1.2 -1.8 1.0 4.2 1970 0.642 -0.40 576 0.9 -0.7 0.3 -1.0 -0.2 1.1 1.0 0.1 -0.2 1.1 -1.5 -5.7 1971 0.717 0.08 571 -0.8 0.0 -0.2 0.5 1.1 -0.4 -0.8 0.0 0.8 0.4 0.0 0.3 1972 -0.533 -1.25 572 -0.4 -4.2 -0.9 -1.9 -0.3 -0.4 -0.6 -0.5 -2.1 -2.8 -0.2 -0.7 1973 1.292 1.83 590 4.1 2.9 3.6 1.6 0.1 0.6 1.4 0.7 1.8 2.7 -1.5 3.9 1974 0.408 -0.88 596 -3.7 -0.1 -4.9 -0.5 -2.0 -0.2 -0.7 -1.1 -1.0 -1.6 3.1 2.1 1975 0.675 0.27 593 2.2 -0.7 0.9 -0.5 2.4 0.2 1.5 -0.1 0.9 0.8 -0.7 -3.7 1976 1.008 0.33 584 0.5 2.1 0.4 2.9 -0.2 -0.2 -1.5 0.6 0.6 -1.0 0.0 -0.2 1977 1.525 0.52 579 0.4 2.7 3.5 -0.1 0.9 0.0 -0.2 -0.7 -0.9 1.6 0.0 -1.0 1978 0.575 -0.95 575 -1.5 -2.5 -2.9 -1.9 -0.9 -0.4 0.2 0.1 -0.2 -1.0 -2.0 1.6 1979 0.858 0.28 571 0.2 -6.2 1.5 -0.2 -0.4 0.2 0.9 0.2 0.9 0.7 3.1 2.5 1980 1.067 0.21 561 0.5 6.3 -1.1 2.6 1.3 -0.3 -0.9 0.0 -1.3 0.0 0.0 -4.6 1981 2.400 1.33 553 3.8 3.0 3.9 -1.7 -0.4 -0.1 0.6 1.3 1.3 -0.5 1.2 3.6 1982 0.200 -2.20 544 -8.0 -5.5 -4.8 -1.9 -0.7 0.1 -0.2 -2.3 -0.1 0.5 -3.7 0.2 1983 1.375 1.17 535 6.4 3.2 1.9 2.2 -1.1 0.8 0.2 2.2 -0.1 0.0 2.8 -4.4 1984 1.292 -0.08 524 -1.4 2.8 -0.6 1.2 0.6 -0.4 0.1 0.1 -1.5 -0.8 -2.4 1.3 1985 0.683 -0.61 511 0.9 -5.2 0.8 -1.9 1.0 -0.9 -0.3 -1.7 0.8 0.1 -3.2 2.3 1986 1.217 0.53 506 1.6 0.8 0.4 0.7 0.1 0.1 -0.8 0.4 -0.6 0.2 1.4 2.1 1987 2.367 1.15 499 0.4 2.6 0.0 1.6 -0.1 1.3 1.0 -0.5 2.2 0.4 4.1 0.8 1988 1.675 -0.69 500 -3.1 -2.4 0.6 -0.6 0.7 0.0 0.1 1.3 -1.1 -0.1 -0.8 -2.9 1989 0.808 -0.87 496 0.3 -1.9 -4.3 -0.8 -0.7 0.0 0.4 0.1 0.6 0.0 -2.0 -2.1 1990 1.133 0.33 277 1.8 0.4 4.3 0.1 -0.7 -0.2 0.0 0.1 0.0 -0.5 -0.1 -1.3 1991 1.500 0.37 44 -3.2 4.4 -1.4 0.5 1.1 0.3 0.0 0.3 -0.4 0.1 1.1 1.6 1992 0.833 -0.67 41 3.5 -2.1 0.4 -2.3 -1.7 -1.5 -1.8 -1.2 -0.9 0.2 -0.2 -0.4 1993 0.925 0.09 41 -1.3 -0.7 0.0 1.6 1.7 0.8 1.1 0.5 0.7 0.3 -0.5 -3.1 1994 1.008 0.08 37 -2.1 -1.6 0.2 -0.1 -0.1 0.9 1.2 0.1 0.9 1.6 0.0 0.0 1995 1.350 0.34 37 3.7 2.2 -0.6 0.4 -3.2 0.5 -0.3 0.0 -0.1 -0.5 0.0 2.0 1996 1.183 -0.17 39 -3.6 1.9 -1.0 -0.1 0.7 -0.7 -0.2 0.0 -0.1 -1.8 0.2 2.7 1997 1.542 0.36 39 1.5 -1.3 -0.8 -0.6 0.7 0.0 0.2 -0.1 0.4 0.5 1.7 2.1 1998 3.000 1.46 44 -0.3 2.7 2.4 2.8 2.9 0.8 0.7 1.1 1.1 1.6 1.7 0.0 1999 2.842 -0.16 48 0.9 0.2 2.0 -0.6 -1.3 -0.6 -1.1 -0.5 -0.2 -1.4 0.0 0.7 2000 2.142 -0.70 48 0.0 -0.6 0.3 -1.4 -1.2 -1.2 0.4 -0.3 -1.4 0.7 -0.4 -3.3 2001 2.725 0.58 48 2.6 -2.3 -1.3 0.3 1.6 0.6 0.0 0.8 1.5 0.2 0.0 3.0 2002 1.808 -0.92 39 -1.8 0.0 -3.8 -2.0 -2.2 0.1 -0.4 -0.5 -0.5 -0.6 -0.5 1.2 2003 2.400 0.59 36 1.6 -1.5 0.4 1.4 2.4 0.0 0.4 0.4 0.6 2.2 0.0 -0.8 2004 1.733 -0.67 36 -4.1 3.2 1.2 0.4 -2.0 -0.5 -0.1 -0.9 -1.3 -1.0 0.4 -3.3 2005 2.525 0.79 44 0.2 -0.1 1.0 1.5 1.8 0.5 0.0 0.2 0.5 0.6 0.6 2.7 2006 3.425 0.90 40 3.7 1.9 2.1 0.8 0.5 1.1 0.3 0.1 0.4 -0.1 -1.2 1.2 2007 2.100 -1.32 44 0.1 -2.1 -3.2 -1.5 -2.5 -0.9 0.2 -0.4 -1.3 -0.2 -0.1 -4.0 2008 2.158 0.06 47 -0.7 -1.1 0.0 -0.1 1.5 0.1 0.0 0.0 0.4 0.1 1.7 -1.2 2009 2.050 -0.11 35 -2.2 1.3 -1.2 -0.3 -1.7 0.0 -1.0 0.0 1.1 -1.8 0.9 3.6 2010 2.392 0.34 35 4.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 For Country Code 403 From input file ./data/v2.mean.inv.1880.dt
South America
The Latin zone has a bit of warming with the move from the rural mountain stations to urban airports, but not much. Half to 3/4 C over 130 years. About what one would expect from UHI and airports. Though 2007 is only 0.36 C and 2000 is 0.29 C so there is room for variation. I’m seeing no reason to panic over 1/2 C of what’s most likely instrument error and UHI issues.
South America dT Cumulative from GHCN
Produced from input file: ./DTemps/Temps.3 Thermometer Records, Average of Monthly dT/dt, Yearly running total by Year Across Month, with a count of thermometer records in that year ----------------------------------------------------------------------------------- YEAR dT dT/yr Count JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC ----------------------------------------------------------------------------------- 1880 0.000 0.00 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1881 -0.133 -0.13 6 -0.5 0.8 0.9 0.2 -0.3 -2.2 -1.5 -1.4 1.5 1.0 -0.2 0.1 1882 -0.383 -0.25 6 1.0 -0.8 -1.4 -0.9 -0.1 0.0 0.2 0.4 -0.4 1.5 -0.2 -2.3 1883 -0.283 0.10 7 -0.7 0.0 0.7 0.6 0.5 0.7 0.9 -0.9 -0.3 -1.3 0.2 0.8 1884 -0.175 0.11 6 0.9 -0.1 0.5 0.6 -1.4 -2.3 -0.7 3.3 0.6 -0.1 -0.1 0.1 1885 -0.575 -0.40 7 -0.5 -0.2 -1.2 -0.7 0.6 0.1 -0.7 -2.8 0.2 0.2 0.9 -0.7 1886 -0.708 -0.13 7 0.2 -0.1 0.9 0.4 -0.2 -0.4 0.3 -0.4 -0.9 -0.7 -1.2 0.5 1887 -0.483 0.23 9 -0.4 -0.2 -0.6 -0.3 -0.3 1.8 0.6 2.3 0.0 0.3 -0.2 -0.3 1888 0.033 0.52 10 0.4 0.7 0.7 0.7 0.1 -1.6 1.0 -0.1 1.5 1.2 0.8 0.8 1889 -0.400 -0.43 10 -0.1 -0.1 0.7 -0.1 1.2 0.2 -0.9 -2.1 -2.2 -1.1 -0.3 -0.4 1890 -0.583 -0.18 10 -0.2 -0.3 -1.2 0.2 -1.4 -0.2 0.2 0.1 0.5 -0.3 0.3 0.1 1891 -0.350 0.23 13 0.0 0.1 0.4 -0.1 0.7 1.2 -0.3 0.9 0.5 0.2 0.0 -0.8 1892 -0.633 -0.28 12 0.6 0.5 0.0 0.0 -0.7 -1.6 -0.1 -0.8 -0.7 0.0 -0.7 0.1 1893 -0.883 -0.25 14 -0.6 -1.2 0.1 -0.4 0.0 0.1 0.4 -0.2 -0.7 -1.0 -0.2 0.7 1894 -0.458 0.42 14 0.8 1.2 -0.5 0.2 0.8 -0.2 -0.8 0.7 1.2 1.3 0.9 -0.5 1895 -0.150 0.31 16 -0.8 -0.3 0.6 0.4 0.2 1.9 0.7 0.8 0.3 -0.2 -0.5 0.6 1896 0.133 0.28 18 0.1 0.2 -0.1 -0.1 0.0 -0.7 0.7 0.9 0.9 0.7 0.7 0.1 1897 -0.142 -0.28 18 0.6 -0.1 0.9 0.9 0.5 0.1 -2.1 -1.9 -1.5 -0.2 -0.6 0.1 1898 -0.375 -0.23 18 0.0 0.4 -0.8 -1.0 -0.8 1.3 0.9 -0.1 0.0 -1.3 -0.6 -0.8 1899 0.008 0.38 18 -0.4 -0.9 0.5 0.6 1.4 -1.7 1.3 1.3 0.5 0.9 0.6 0.5 1900 0.250 0.24 20 0.5 0.8 0.0 -0.1 -0.5 1.3 0.1 -0.2 0.5 0.4 0.4 -0.3 1901 0.067 -0.18 29 -0.5 -0.4 -0.4 -0.5 0.0 0.1 -0.5 -0.2 0.1 0.2 -0.1 0.0 1902 0.025 -0.04 30 0.2 0.7 0.1 0.9 0.5 -0.1 -0.4 -1.3 -0.7 -0.6 0.2 0.0 1903 -0.175 -0.20 31 -0.5 -0.7 0.0 -0.9 -0.7 -0.4 0.0 1.0 0.5 -0.1 -0.2 -0.4 1904 -0.183 -0.01 32 0.3 -0.4 -0.5 0.4 -0.2 0.0 0.0 0.2 0.1 0.1 0.0 -0.1 1905 -0.233 -0.05 33 -0.3 -0.2 0.3 -0.2 0.0 0.0 -0.3 0.0 -0.3 0.3 0.1 0.0 1906 -0.208 0.03 34 0.2 0.8 0.1 0.1 0.1 -1.3 0.3 0.3 -0.4 0.2 0.0 -0.1 1907 -0.492 -0.28 33 0.0 -0.1 -0.2 -0.3 -0.4 0.3 -0.2 -1.1 -0.4 -0.8 -0.2 0.0 1908 -0.242 0.25 32 -0.3 -0.1 0.2 -0.3 0.3 1.3 0.8 0.5 0.8 0.0 -0.4 0.2 1909 -0.333 -0.09 34 0.3 -0.3 -0.3 0.6 -0.6 -0.9 -0.3 0.7 0.0 0.2 0.0 -0.5 1910 -0.375 -0.04 36 -0.4 -0.1 -0.6 -0.6 0.2 0.4 -0.5 -0.1 -0.1 0.0 0.6 0.7 1911 -0.383 -0.01 36 0.2 0.1 0.2 0.0 0.5 -0.3 0.5 -0.6 -0.5 -0.2 -0.1 0.1 1912 -0.175 0.21 37 0.4 0.4 0.7 0.5 -0.3 0.5 -0.4 0.0 0.4 0.6 -0.3 0.0 1913 0.050 0.23 38 -0.1 0.1 -0.3 0.3 0.2 -0.3 1.2 0.6 0.6 0.0 0.5 -0.1 1914 -0.008 -0.06 40 0.1 0.1 0.5 -0.3 0.1 0.5 0.0 -0.1 -0.6 -0.1 -0.8 -0.1 1915 0.008 0.02 41 -0.3 0.1 0.0 0.3 0.3 -1.4 -0.3 0.4 0.2 0.1 0.7 0.1 1916 -0.333 -0.34 41 0.2 -0.5 -0.8 0.1 -0.1 -0.4 -1.6 -0.6 0.1 0.0 0.0 -0.5 1917 -0.325 0.01 41 -0.2 0.0 0.0 -0.5 -1.2 1.2 0.9 -0.2 -0.2 -0.2 0.0 0.5 1918 -0.350 -0.03 41 0.0 0.0 0.2 0.2 0.0 -0.2 -0.2 0.0 -0.5 0.0 -0.1 0.3 1919 0.067 0.42 40 0.7 0.3 0.8 0.0 2.1 0.6 0.8 0.2 0.0 -0.1 -0.3 -0.1 1920 -0.150 -0.22 40 -0.2 -0.1 -0.4 0.4 -1.1 -0.5 -1.0 0.1 0.4 -0.2 0.3 -0.3 1921 -0.375 -0.22 41 -0.5 -0.2 -0.4 -0.6 0.0 -0.8 -0.1 0.0 -0.3 0.2 -0.2 0.2 1922 -0.217 0.16 42 0.0 0.1 0.1 0.0 -0.2 0.2 1.3 0.1 0.5 -0.4 0.3 -0.1 1923 -0.458 -0.24 42 0.0 0.3 0.3 0.0 -0.6 0.8 -1.9 -0.5 -0.4 -0.2 -0.3 -0.4 1924 -0.400 0.06 42 0.0 -0.4 0.0 -0.2 0.0 0.0 0.7 -0.1 -0.2 0.8 -0.4 0.5 1925 -0.192 0.21 42 -0.1 0.3 0.0 0.4 0.2 -0.6 0.0 1.2 0.0 -0.1 0.8 0.4 1926 0.017 0.21 42 0.4 0.5 0.4 -0.1 0.1 0.9 0.2 -0.2 0.3 0.2 0.0 -0.2 1927 -0.150 -0.17 42 -0.2 0.0 -0.2 0.4 0.0 -0.7 0.0 -0.3 -0.4 0.0 -0.1 -0.5 1928 -0.258 -0.11 42 -0.2 -0.8 -0.5 -0.1 0.0 0.0 0.1 -0.1 0.4 0.0 0.0 -0.1 1929 -0.133 0.13 41 0.4 0.3 -0.1 0.1 -0.2 0.4 0.4 0.0 0.2 0.0 -0.2 0.2 1930 -0.100 0.03 40 0.0 0.1 0.2 0.0 0.7 1.0 -0.8 -0.6 0.0 -0.5 0.0 0.3 1931 -0.133 -0.03 86 0.0 0.1 0.1 0.0 -0.4 -0.3 0.1 0.3 -0.4 0.4 -0.3 0.0 1932 0.417 0.55 86 0.6 -0.4 0.2 0.6 1.0 0.7 1.6 -0.3 1.2 -0.2 1.7 -0.1 1933 0.133 -0.28 90 -0.7 -0.3 -0.7 0.0 1.0 -0.2 -2.3 1.1 0.0 0.0 -0.8 -0.5 1934 -0.050 -0.18 89 0.8 -0.7 0.1 -1.3 -0.4 0.4 1.2 -0.2 -0.5 -0.9 -0.6 -0.1 1935 0.142 0.19 89 -0.9 0.8 1.1 0.4 1.2 0.3 -0.3 -0.2 -0.4 -0.9 0.9 0.3 1936 0.158 0.02 89 0.0 -0.2 -0.8 0.8 -1.1 -0.5 0.7 -0.9 0.8 1.4 -0.3 0.3 1937 0.083 -0.08 88 0.0 0.7 -0.4 -0.2 -0.6 0.7 -0.8 0.8 -0.3 -0.8 0.0 0.0 1938 -0.075 -0.16 88 -0.6 -0.7 -0.3 -1.0 0.8 -0.7 0.2 -1.1 0.3 0.9 -0.1 0.4 1939 0.117 0.19 90 1.2 -0.1 0.1 0.1 0.0 0.8 0.5 1.9 -0.2 0.0 -0.7 -1.3 1940 0.183 0.07 91 -0.7 0.2 0.0 0.9 0.5 0.1 0.8 -1.3 0.1 -1.1 0.5 0.8 1941 0.150 -0.03 109 0.2 -0.3 -0.2 -0.2 -0.9 -0.4 -0.3 0.9 -0.7 1.4 0.0 0.1 1942 0.033 -0.12 109 0.4 0.7 0.1 0.1 -0.4 -1.6 -2.0 -0.6 1.1 -0.7 1.0 0.5 1943 0.292 0.26 112 -0.2 0.4 0.1 0.0 1.2 1.4 2.0 -1.5 0.0 0.7 -0.9 -0.1 1944 0.575 0.28 111 -0.8 -0.4 0.3 0.1 -0.3 0.3 -0.2 2.2 1.3 0.0 0.3 0.6 1945 0.550 -0.03 113 1.0 -0.4 0.1 0.9 0.0 -0.6 -0.7 0.7 -0.7 0.5 -0.1 -1.0 1946 0.117 -0.43 113 -1.3 0.3 -0.7 -0.6 -0.1 -0.2 -0.1 -1.1 -0.1 -1.3 0.4 -0.4 1947 0.142 0.02 111 0.4 -0.1 0.2 -0.4 0.0 1.5 -0.2 -0.8 -0.8 0.1 0.3 0.1 1948 0.150 0.01 107 0.4 -0.4 -0.6 0.0 -0.1 -0.3 0.4 -0.5 0.8 -0.1 -0.7 1.2 1949 0.108 -0.04 129 -0.1 -0.1 0.4 0.2 0.0 -0.2 -0.1 0.5 -0.6 -0.4 0.5 -0.6 1950 0.083 -0.03 136 -0.2 0.4 0.0 0.0 0.2 0.0 0.2 0.4 -0.3 0.0 -1.0 0.0 1951 0.183 0.10 191 0.0 -0.7 -0.1 -0.5 0.4 0.1 0.4 0.3 0.4 0.4 0.5 0.0 1952 0.217 0.03 194 0.8 1.2 1.0 0.3 -0.2 -1.8 -0.3 -0.2 -0.1 -0.1 -0.2 0.0 1953 0.325 0.11 200 -0.4 0.2 -0.2 0.1 -0.1 1.4 -1.5 0.9 1.0 -0.5 0.3 0.1 1954 -0.042 -0.37 201 -0.2 -0.3 0.0 0.0 -1.1 -0.5 0.3 -1.2 -1.4 0.0 0.1 -0.1 1955 -0.208 -0.17 205 0.6 -0.3 -1.0 -0.2 0.0 0.0 -0.7 -0.3 0.1 -0.2 0.1 -0.1 1956 -0.383 -0.17 208 -1.4 -0.4 0.4 -0.6 -0.8 -0.4 1.2 0.0 0.0 0.4 -0.5 0.0 1957 0.133 0.52 209 0.9 0.0 0.9 0.5 2.7 0.5 -0.4 0.4 -0.1 0.4 0.0 0.4 1958 0.325 0.19 213 0.4 0.7 -0.3 0.6 -1.3 0.8 2.1 -0.7 0.6 0.1 0.0 -0.7 1959 0.075 -0.25 218 -0.8 0.0 -0.3 -0.6 0.0 -0.6 -0.8 0.2 -0.1 -0.4 -0.1 0.5 1960 0.158 0.08 218 0.8 0.1 0.0 0.3 -0.3 0.0 -0.7 0.4 0.0 0.1 0.2 0.1 1961 0.258 0.10 247 -0.1 -0.2 -0.1 0.0 0.9 -0.3 0.0 0.7 0.0 0.2 0.1 0.0 1962 0.117 -0.14 246 -0.3 0.0 0.5 0.0 -0.8 0.0 -0.9 -0.9 0.4 -0.6 0.5 0.4 1963 0.308 0.19 244 0.1 0.1 -0.5 0.5 0.4 0.5 1.9 0.5 -0.2 0.4 -1.1 -0.3 1964 -0.092 -0.40 242 0.3 0.0 -0.2 -0.2 0.0 -1.1 -1.4 -0.7 -0.3 -0.7 0.0 -0.5 1965 0.258 0.35 247 -0.3 0.1 0.0 -0.1 -0.3 2.3 0.6 0.5 0.0 0.8 0.5 0.1 1966 0.108 -0.15 247 0.2 -0.6 0.2 0.4 0.3 -0.8 0.1 -0.8 -0.3 -0.5 0.0 0.0 1967 0.117 0.01 248 -0.4 0.2 -0.3 0.0 0.7 -1.7 -0.2 0.5 0.5 0.3 -0.2 0.7 1968 0.092 -0.03 251 0.0 0.0 0.0 -1.1 -1.6 1.1 0.8 0.3 -0.1 -0.2 0.7 -0.2 1969 0.458 0.37 259 0.2 0.3 0.8 1.3 1.5 0.4 -0.1 -0.6 0.8 -0.2 -0.4 0.4 1970 0.267 -0.19 262 -0.1 0.0 -0.2 0.4 -0.5 -0.3 -0.5 0.0 -0.2 0.2 -0.6 -0.5 1971 -0.067 -0.33 283 -0.5 -0.9 -0.3 -1.2 -0.7 -0.8 0.3 0.1 0.0 -0.3 0.3 0.0 1972 0.317 0.38 284 0.6 0.6 0.0 0.4 1.2 1.9 -0.1 -0.2 -0.1 0.1 0.0 0.2 1973 0.200 -0.12 289 0.4 0.5 0.8 0.6 -0.5 -0.5 -0.6 -0.3 -0.7 0.1 -0.5 -0.7 1974 -0.100 -0.30 288 -0.8 -1.2 -0.7 -0.5 0.1 -0.6 0.5 0.1 -0.3 -0.3 0.2 -0.1 1975 -0.050 0.05 304 0.0 0.4 0.0 0.2 -0.1 0.7 -1.0 -0.1 0.3 0.1 -0.3 0.4 1976 -0.125 -0.07 299 0.0 -0.1 -0.9 -0.5 -0.1 -0.8 0.6 0.2 0.0 0.2 0.4 0.1 1977 0.342 0.47 304 0.4 0.4 1.2 0.5 0.0 0.7 0.7 0.2 0.7 0.5 0.2 0.1 1978 0.275 -0.07 300 -0.1 0.1 0.0 0.0 0.2 -0.4 0.2 -0.6 -0.1 -0.1 0.0 0.0 1979 0.250 -0.02 300 0.3 0.0 -0.4 -0.1 0.0 -0.1 -0.4 1.6 -0.6 0.0 -0.5 -0.1 1980 0.392 0.14 301 0.1 0.0 1.1 0.7 0.8 0.1 -0.5 -0.7 0.0 -0.2 0.0 0.3 1981 0.300 -0.09 276 -0.3 0.0 -0.8 -0.3 0.3 -0.2 -0.1 0.2 -0.2 -0.1 0.5 -0.1 1982 0.425 0.12 232 0.2 -0.4 0.0 0.1 -0.4 0.4 0.4 0.0 0.8 0.1 -0.1 0.4 1983 0.483 0.06 225 0.6 0.6 0.3 0.2 0.0 -0.6 -0.2 -0.4 -0.7 0.3 0.4 0.2 1984 0.142 -0.34 225 -0.3 -0.2 -0.3 -0.8 -0.4 -0.2 -0.1 -0.7 0.4 0.2 -0.5 -1.2 1985 0.342 0.20 230 -0.2 -0.1 0.0 0.0 0.2 1.0 0.3 0.6 0.0 -0.5 0.6 0.5 1986 0.417 0.08 228 0.2 0.0 -0.4 0.6 0.0 0.3 0.1 0.3 0.0 -0.1 -0.3 0.2 1987 0.542 0.12 231 0.1 0.3 0.6 0.2 -0.6 0.0 0.6 -0.2 0.0 0.2 0.4 -0.1 1988 0.233 -0.31 237 -0.1 -0.2 0.3 -0.7 0.0 -0.8 -1.7 0.1 -0.2 -0.6 -0.1 0.3 1989 0.375 0.14 229 0.0 0.3 -0.8 0.0 0.4 0.8 0.4 0.4 0.0 0.2 0.0 0.0 1990 0.383 0.01 213 0.0 -0.4 0.0 0.1 0.1 -0.5 0.0 0.2 0.1 0.6 0.2 -0.3 1991 0.508 0.12 212 -0.2 0.0 0.3 0.1 0.9 0.4 0.1 -0.1 0.6 -0.5 -0.2 0.1 1992 0.392 -0.12 195 0.1 0.0 0.0 -0.1 -0.4 0.3 -0.4 -0.2 -0.2 0.0 -0.5 0.0 1993 0.467 0.08 198 0.0 0.0 0.1 0.2 -0.1 -0.1 0.3 0.1 -0.1 0.3 0.1 0.1 1994 0.633 0.17 203 0.0 -0.1 -0.4 -0.1 0.8 0.4 0.1 0.0 0.6 -0.2 0.2 0.7 1995 0.550 -0.08 198 0.0 -0.1 -0.1 0.1 -0.4 -0.5 0.1 -0.3 -0.1 0.0 0.3 0.0 1996 0.467 -0.08 203 -0.1 0.1 0.2 -0.1 -0.1 -0.9 -0.4 0.7 0.0 0.1 0.0 -0.5 1997 0.675 0.21 203 0.3 -0.2 -0.1 0.3 0.2 0.6 0.8 0.0 0.3 0.2 0.0 0.1 1998 0.725 0.05 201 0.0 0.3 0.0 0.1 0.0 0.5 0.6 -0.2 -0.7 0.2 -0.2 0.0 1999 0.367 -0.36 198 -0.4 0.1 -0.1 -1.0 -0.6 -0.6 -1.2 -0.2 0.4 -0.5 -0.1 -0.1 2000 0.292 -0.07 196 0.3 -0.2 -0.4 0.4 -0.1 0.5 -0.6 0.0 -0.7 0.1 -0.2 0.0 2001 0.667 0.38 192 0.0 0.5 0.6 0.1 0.0 -0.1 1.1 1.1 0.4 0.2 0.3 0.3 2002 0.708 0.04 185 0.1 0.0 0.2 0.0 0.3 -0.7 0.0 -0.1 0.2 0.3 0.2 0.0 2003 0.783 0.08 186 0.3 0.2 0.0 0.0 0.0 1.2 0.0 -0.7 0.1 0.0 0.0 -0.2 2004 0.700 -0.08 181 0.0 -0.3 0.2 0.5 -1.1 -0.1 0.0 0.0 0.3 -0.4 -0.3 0.2 2005 0.692 -0.01 180 -0.1 0.3 -0.3 -0.6 1.0 0.2 0.1 0.3 -0.9 -0.2 0.4 -0.3 2006 0.792 0.10 181 0.0 -0.1 -0.1 0.1 -0.5 -0.1 0.7 -0.2 0.4 0.7 -0.3 0.6 2007 0.367 -0.42 174 0.0 -0.1 0.0 0.3 -0.5 -0.9 -1.8 -1.1 0.2 -0.5 -0.3 -0.4 2008 0.650 0.28 168 -0.2 0.0 -0.1 -0.5 0.6 -0.1 1.7 1.0 -0.3 -0.1 1.2 0.2 2009 0.775 0.12 168 0.0 0.0 0.5 0.9 0.8 0.2 -1.2 0.7 0.0 -0.1 -0.1 -0.2 2010 0.808 0.03 106 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 For Country Code 3 From input file ./data/v2.mean.inv.1880.dt
Africa?
Shows a very similar pattern. Substantially nothing until The Great Dying of Thermometers in 1990. Then a modest ‘lift’ through 2003 as thermometers are slowly pruned from the series.
Africa dT Cumulative from GHCN
Produced from input file: ./DTemps/Temps.1 Thermometer Records, Average of Monthly dT/dt, Yearly running total by Year Across Month, with a count of thermometer records in that year ----------------------------------------------------------------------------------- YEAR dT dT/yr Count JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC ----------------------------------------------------------------------------------- 1880 0.000 0.00 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1881 0.333 0.33 22 2.4 0.7 1.2 0.8 0.0 -0.1 -0.3 0.7 0.2 -0.8 -0.6 -0.2 1882 -0.542 -0.87 22 -2.6 -1.6 -1.8 -1.2 0.3 0.0 -0.7 -0.8 -1.2 -0.7 -0.2 0.0 1883 -0.567 -0.02 21 0.6 0.8 0.0 -0.5 -0.5 -0.1 0.0 -0.4 0.6 0.0 0.0 -0.8 1884 -0.783 -0.22 21 -0.8 0.3 -0.1 0.0 -0.3 -0.8 -0.6 0.5 -0.5 -0.4 -0.3 0.4 1885 -0.483 0.30 22 -0.3 1.1 0.5 -0.6 0.6 0.5 0.7 0.5 0.2 0.0 0.1 0.3 1886 -0.483 0.00 26 0.0 -1.1 0.8 1.2 -0.1 0.3 -0.1 -1.0 0.1 0.6 -0.4 -0.3 1887 -0.525 -0.04 27 -0.4 -0.8 -0.3 -0.8 0.1 0.1 0.2 0.6 0.4 -0.6 0.9 0.1 1888 -0.367 0.16 33 0.4 0.2 0.0 1.1 -0.1 0.0 0.0 -0.7 -0.1 0.7 -0.2 0.6 1889 -0.467 -0.10 38 0.0 0.4 -0.6 -0.6 0.2 -0.2 0.2 1.0 -0.1 0.2 -0.2 -1.5 1890 -0.717 -0.25 37 0.2 -0.4 -0.3 -0.3 -0.6 0.0 -0.5 0.0 -0.4 -0.7 -0.3 0.3 1891 -0.392 0.33 38 -1.2 -1.2 0.6 1.1 0.2 0.2 0.8 -0.5 0.8 1.2 1.3 0.6 1892 -0.050 0.34 45 1.5 1.7 1.0 -0.4 0.0 0.6 -0.2 0.1 0.0 -0.1 -0.2 0.1 1893 0.025 0.08 42 -0.6 -0.4 0.0 0.3 0.8 0.0 0.0 0.0 0.7 0.1 0.4 -0.4 1894 -0.175 -0.20 44 0.0 -0.5 -0.8 -0.4 -0.7 -0.5 -0.1 0.2 -0.2 0.5 0.0 0.1 1895 0.217 0.39 46 0.0 1.6 0.1 1.1 0.1 -0.3 0.3 0.0 -0.1 0.1 0.8 1.0 1896 -0.325 -0.54 46 0.1 -1.1 0.0 -1.3 -0.6 0.2 0.0 -0.4 0.4 -1.3 -2.0 -0.5 1897 0.117 0.44 51 0.2 0.2 1.0 1.1 1.0 0.4 0.2 0.8 -0.3 0.2 0.6 -0.1 1898 -0.100 -0.22 58 0.0 -0.4 -0.8 -0.6 -0.2 0.1 -0.5 -0.4 -0.2 0.2 0.4 -0.2 1899 0.242 0.34 57 0.1 1.0 0.1 0.9 0.5 -0.6 -0.1 0.3 0.6 0.7 0.1 0.5 1900 0.067 -0.18 53 -0.3 0.6 0.0 -0.3 -0.2 0.1 -0.3 -0.3 -0.3 -0.2 -0.6 -0.3 1901 -0.117 -0.18 61 -0.1 -1.1 0.0 0.3 -0.6 0.6 0.3 0.3 -0.4 -1.3 0.0 -0.2 1902 -0.042 0.08 66 -0.3 0.6 0.0 0.0 0.0 -0.6 0.4 0.3 0.2 0.1 0.0 0.2 1903 -0.292 -0.25 63 0.4 -0.9 0.0 -0.6 0.2 -0.3 -0.8 -0.4 -0.3 0.0 -0.3 0.0 1904 -0.292 -0.00 68 -0.4 0.1 -0.4 -0.1 0.0 0.5 0.3 0.0 0.0 0.1 0.1 -0.2 1905 -0.200 0.09 72 -0.1 -0.9 0.0 0.6 0.0 0.0 0.1 0.0 0.3 0.2 0.7 0.2 1906 -0.233 -0.03 78 0.7 0.5 0.2 -0.6 0.0 0.0 -0.3 -0.2 -0.1 -0.1 -0.3 -0.2 1907 -0.450 -0.22 83 -0.7 -0.3 -0.5 -0.2 -0.2 -0.1 -0.1 -0.1 -0.3 -0.1 -0.2 0.2 1908 -0.217 0.23 73 0.5 0.2 0.3 0.0 0.9 0.0 0.1 0.0 0.6 0.3 0.1 -0.2 1909 -0.200 0.02 74 -0.6 0.0 0.4 0.1 -0.3 -0.1 -0.1 0.1 -0.2 0.1 0.3 0.5 1910 -0.350 -0.15 88 0.1 0.4 -0.8 0.3 -0.7 0.0 0.1 -0.1 -0.5 0.0 -0.4 -0.2 1911 -0.275 0.08 96 0.0 -0.4 0.2 -0.3 0.0 -0.1 -0.2 0.1 0.5 0.2 0.3 0.6 1912 -0.142 0.13 92 0.5 1.1 0.7 0.2 1.0 0.4 0.2 -0.1 -0.7 -0.5 -0.3 -0.9 1913 -0.083 0.06 99 0.3 -0.6 -0.7 0.0 -0.4 0.0 0.1 0.3 0.8 0.3 0.4 0.2 1914 0.050 0.13 100 -0.1 0.2 1.0 0.2 0.4 0.1 -0.1 -0.3 0.0 0.0 0.1 0.1 1915 0.125 0.08 78 0.0 0.2 0.1 0.2 -0.2 0.0 0.2 0.4 -0.2 0.0 0.1 0.1 1916 -0.075 -0.20 73 -0.8 -0.1 0.0 0.0 -0.2 0.0 0.0 -0.5 -0.2 -0.4 -0.1 -0.1 1917 -0.250 -0.17 73 0.5 -0.1 -0.5 -0.4 -0.4 -0.4 0.0 0.0 0.0 -0.2 -0.1 -0.5 1918 -0.267 -0.02 74 -0.8 -0.5 -0.2 -0.3 0.1 -0.1 0.0 -0.2 0.6 0.7 0.2 0.3 1919 -0.008 0.26 78 1.1 0.9 0.7 0.8 0.0 0.2 -0.1 0.2 -0.4 0.0 -0.1 -0.2 1920 -0.233 -0.23 82 -0.4 -1.0 -1.0 -0.1 0.3 -0.1 0.0 0.1 0.0 -0.3 -0.2 0.0 1921 -0.225 0.01 91 -0.2 0.2 0.0 -0.3 0.0 0.0 0.0 -0.1 0.1 0.1 0.0 0.3 1922 -0.008 0.22 90 0.3 0.4 0.6 0.6 0.1 0.3 0.0 0.2 0.0 0.0 0.3 -0.2 1923 -0.150 -0.14 104 -0.1 -0.3 -0.2 -0.8 -0.1 -0.2 -0.2 -0.1 -0.1 0.1 0.1 0.2 1924 0.033 0.18 110 0.2 0.1 0.5 0.4 0.6 0.5 0.3 0.0 0.2 -0.1 -0.5 0.0 1925 -0.167 -0.20 114 -1.2 -0.4 -0.6 0.0 -0.4 -0.4 -0.2 0.1 -0.1 -0.1 0.3 0.6 1926 0.108 0.28 121 1.2 1.0 0.4 0.3 0.2 0.2 0.0 -0.1 0.3 0.5 0.0 -0.7 1927 0.125 0.02 119 -0.1 -0.9 -0.2 -0.3 0.0 -0.1 0.4 0.2 0.0 0.0 0.4 0.8 1928 0.083 -0.04 118 0.3 0.1 0.2 0.3 -0.3 0.1 -0.2 -0.1 0.1 -0.1 -0.3 -0.6 1929 -0.117 -0.20 120 -0.6 0.0 -0.5 -0.3 0.2 -0.1 -0.2 -0.3 -0.1 -0.2 0.0 -0.3 1930 -0.125 -0.01 123 -0.1 0.0 0.0 -0.1 -0.4 -0.3 0.1 0.1 -0.4 0.0 0.1 0.9 1931 0.175 0.30 133 0.6 0.2 0.8 0.3 0.7 0.7 0.4 0.5 0.1 0.1 -0.2 -0.6 1932 -0.150 -0.33 133 -0.4 -0.3 -0.7 0.0 -0.5 -0.5 -0.8 -0.8 0.0 0.0 0.0 0.1 1933 -0.083 0.07 140 0.2 0.6 0.1 0.0 0.2 0.1 0.3 0.1 -0.4 0.0 -0.1 -0.3 1934 -0.200 -0.12 138 -0.7 -1.2 -0.7 0.0 0.0 0.3 0.2 0.4 0.2 -0.3 0.0 0.4 1935 -0.292 -0.09 134 0.3 0.2 0.3 -0.3 -0.4 -0.4 -0.2 -0.8 0.2 0.0 -0.1 0.1 1936 -0.208 0.08 139 0.5 1.3 0.4 0.3 -0.2 -0.2 0.0 0.1 -0.4 -0.2 0.0 -0.6 1937 -0.033 0.18 137 -0.3 -0.2 0.0 0.0 0.3 0.5 -0.2 0.3 0.4 0.3 0.7 0.3 1938 -0.267 -0.23 143 0.0 -0.7 -0.8 -0.1 -0.1 -0.1 0.1 0.0 -0.2 0.0 -0.9 0.0 1939 -0.333 -0.07 142 0.1 0.0 -0.2 -0.6 -0.1 -0.3 0.0 -0.1 -0.1 0.1 0.0 0.4 1940 -0.100 0.23 143 0.0 0.6 0.8 0.5 0.2 0.3 -0.1 0.3 0.4 -0.2 0.1 -0.1 1941 0.008 0.11 173 0.0 0.2 0.1 0.4 0.2 0.2 0.2 0.1 -0.2 0.2 0.2 -0.3 1942 -0.067 -0.08 169 -0.2 -0.1 0.0 0.0 0.0 0.2 -0.3 -0.4 0.0 -0.4 0.0 0.3 1943 -0.392 -0.33 180 -0.1 -1.0 -0.7 -0.7 -0.2 -0.7 0.0 -0.2 -0.2 0.2 -0.3 0.0 1944 -0.217 0.17 175 0.0 0.0 0.0 0.4 0.0 0.6 0.3 0.6 0.2 0.0 0.0 0.0 1945 -0.208 0.01 188 0.0 0.3 0.0 0.5 0.1 -0.4 -0.1 -0.3 0.2 -0.3 0.1 0.0 1946 -0.192 0.02 197 0.1 0.1 0.2 -0.3 0.0 0.1 -0.1 -0.2 0.0 0.2 0.1 0.0 1947 -0.025 0.17 201 0.0 0.7 0.5 0.0 0.0 0.3 0.0 0.4 -0.1 0.2 0.0 0.0 1948 -0.325 -0.30 210 0.0 -0.5 -0.9 -0.4 0.1 -0.3 0.0 -0.3 -0.3 -0.3 -0.6 -0.1 1949 -0.242 0.08 234 0.1 -0.5 0.1 0.1 0.0 0.2 0.1 0.0 0.3 0.1 0.4 0.1 1950 -0.383 -0.14 252 -0.3 0.2 0.2 0.0 -0.3 0.0 0.0 -0.3 -0.2 -0.6 -0.3 -0.1 1951 -0.367 0.02 379 -0.1 0.0 0.1 0.0 0.1 -0.1 0.0 0.2 0.1 0.2 0.0 -0.3 1952 -0.258 0.11 394 0.2 0.1 0.0 0.0 0.0 0.2 0.1 -0.1 -0.1 0.2 0.1 0.6 1953 -0.367 -0.11 403 0.3 0.2 -0.4 0.0 0.0 -0.6 -0.2 -0.1 0.0 -0.1 0.0 -0.4 1954 -0.500 -0.13 420 -0.6 -0.2 0.2 -0.3 -0.1 -0.1 -0.4 -0.3 0.0 0.0 0.0 0.2 1955 -0.383 0.12 429 0.5 0.3 0.0 0.0 0.1 0.1 0.5 0.2 -0.1 0.0 0.0 -0.2 1956 -0.550 -0.17 430 -0.6 -0.2 -0.1 0.0 -0.2 0.0 -0.3 0.0 0.0 -0.2 -0.2 -0.2 1957 -0.375 0.18 432 -0.2 -0.4 0.0 0.0 0.0 0.0 0.4 0.3 0.5 0.3 0.5 0.7 1958 -0.142 0.23 432 1.1 0.3 0.7 0.8 0.3 -0.1 -0.4 -0.2 0.0 0.0 0.0 0.3 1959 -0.283 -0.14 433 -0.3 -0.3 -0.3 -0.3 0.0 0.4 0.4 0.0 -0.3 -0.1 -0.4 -0.5 1960 -0.192 0.09 479 -0.2 0.5 -0.2 -0.3 -0.1 0.0 -0.2 0.5 0.2 0.4 0.0 0.5 1961 -0.342 -0.15 485 0.0 -0.5 0.0 0.2 0.3 0.0 0.1 -0.5 -0.1 -0.4 -0.2 -0.7 1962 -0.283 0.06 483 -0.2 0.2 0.2 -0.2 -0.6 -0.3 0.1 0.3 0.1 0.3 0.5 0.3 1963 -0.225 0.06 496 0.5 0.6 -0.4 -0.1 -0.2 0.2 0.0 0.1 0.4 -0.1 -0.3 0.0 1964 -0.467 -0.24 498 -0.3 -0.3 0.9 0.0 0.3 -0.4 -0.7 -0.5 -0.9 -0.4 -0.3 -0.3 1965 -0.458 0.01 496 -0.2 -0.2 -0.6 -0.3 -0.1 0.0 0.6 0.4 0.4 0.1 0.0 0.0 1966 -0.183 0.28 503 0.6 0.1 0.0 0.4 0.2 0.5 0.3 0.3 0.0 0.3 0.3 0.3 1967 -0.492 -0.31 500 -0.7 0.0 -0.3 0.0 0.0 -0.3 -0.7 -0.5 -0.2 -0.3 -0.4 -0.3 1968 -0.425 0.07 505 0.0 -0.1 -0.1 -0.3 -0.2 -0.4 0.3 0.5 0.3 0.1 0.2 0.5 1969 -0.008 0.42 511 0.5 1.0 1.4 0.6 0.5 0.8 0.0 -0.1 0.0 0.0 0.3 0.0 1970 -0.142 -0.13 511 0.4 -0.3 -0.7 0.1 -0.1 0.0 0.0 0.0 -0.1 -0.1 -0.3 -0.5 1971 -0.442 -0.30 466 -0.7 -0.6 0.0 -0.3 -0.3 -0.3 -0.2 -0.3 -0.3 -0.2 -0.3 -0.1 1972 -0.242 0.20 458 0.1 0.0 -0.2 -0.1 -0.1 -0.2 0.4 0.3 0.6 0.6 0.3 0.7 1973 -0.008 0.23 458 0.4 0.8 0.5 0.5 0.5 0.6 0.1 0.0 -0.1 0.0 -0.1 -0.4 1974 -0.433 -0.43 455 -0.7 -0.8 -0.5 -0.4 -0.4 -0.3 -0.7 -0.2 -0.5 -0.3 0.0 -0.3 1975 -0.442 -0.01 457 -0.1 0.0 0.0 0.0 0.0 -0.1 0.1 -0.1 0.1 -0.2 0.0 0.2 1976 -0.467 -0.02 449 0.0 -0.1 -0.1 -0.2 -0.2 -0.2 0.0 0.0 0.1 0.0 0.0 0.4 1977 -0.133 0.33 436 0.8 0.5 0.3 0.5 0.4 0.4 0.3 0.2 0.1 0.2 0.3 0.0 1978 -0.183 -0.05 443 0.0 0.4 0.2 -0.3 0.0 -0.3 -0.3 0.1 -0.2 -0.1 -0.2 0.1 1979 0.000 0.18 431 0.4 0.0 0.1 0.4 0.0 0.2 0.2 0.1 0.1 0.5 0.4 -0.2 1980 -0.042 -0.04 433 -0.1 -0.1 0.0 0.0 0.0 0.0 0.0 -0.1 0.1 -0.1 0.0 -0.2 1981 -0.092 -0.05 409 -0.6 -0.2 0.0 0.0 0.0 0.1 0.0 -0.3 -0.1 0.0 0.0 0.5 1982 -0.075 0.02 377 0.3 0.0 -0.1 -0.3 0.0 0.1 0.4 0.2 0.4 0.0 -0.4 -0.4 1983 0.192 0.27 373 -0.4 0.4 0.3 0.9 0.5 0.1 0.1 0.1 0.0 0.1 0.8 0.3 1984 0.033 -0.16 366 0.1 -0.2 -0.1 -0.5 -0.2 -0.3 -0.2 0.1 -0.1 0.0 -0.3 -0.2 1985 0.050 0.02 361 0.4 0.1 -0.1 -0.3 -0.1 0.1 -0.2 0.0 -0.1 0.2 0.2 0.0 1986 -0.017 -0.07 359 -0.4 0.1 -0.1 0.2 0.3 0.0 0.0 0.0 0.0 -0.2 -0.6 -0.1 1987 0.267 0.28 366 0.2 0.2 0.2 0.2 0.3 0.1 0.5 0.1 0.4 0.2 0.4 0.6 1988 0.083 -0.18 361 0.1 -0.1 0.2 0.0 -0.1 -0.1 -0.2 -0.2 -0.4 -0.2 -0.3 -0.9 1989 -0.108 -0.19 356 -1.0 -0.8 -0.4 -0.5 -0.4 0.0 -0.1 0.0 0.1 -0.2 0.2 0.8 1990 0.067 0.17 346 0.4 0.2 0.1 0.4 0.0 0.1 0.0 0.0 0.3 0.6 0.2 -0.2 1991 -0.033 -0.10 339 0.3 0.2 0.0 -0.2 -0.5 -0.1 0.0 0.0 0.0 -0.4 -0.3 -0.2 1992 -0.108 -0.07 280 -0.2 -0.1 0.0 0.0 0.2 -0.2 -0.3 -0.1 -0.2 0.1 -0.2 0.1 1993 0.017 0.12 293 -0.1 -0.2 0.0 0.0 0.3 0.4 0.3 0.2 -0.1 0.2 0.5 0.0 1994 0.033 0.02 293 0.0 0.2 0.2 0.0 0.2 0.0 0.0 0.0 0.0 -0.3 0.0 -0.1 1995 0.125 0.09 251 0.0 0.1 -0.1 0.0 0.0 0.0 0.2 0.2 0.1 0.2 0.0 0.4 1996 0.175 0.05 283 1.2 0.2 0.0 0.0 -0.1 -0.2 -0.1 -0.1 0.0 -0.3 -0.3 0.3 1997 0.292 0.12 272 0.0 0.0 -0.2 -0.1 -0.1 0.3 0.2 0.0 0.3 0.5 0.5 0.0 1998 0.483 0.19 270 -0.3 0.9 0.5 1.1 0.2 0.1 0.3 0.1 0.1 -0.3 0.0 -0.4 1999 0.425 -0.06 259 0.0 -1.0 0.0 -0.3 0.5 0.2 -0.3 0.0 -0.1 0.3 -0.1 0.1 2000 0.292 -0.13 247 -0.4 -0.1 0.0 0.0 -0.1 -0.4 0.0 -0.1 0.0 -0.6 0.0 0.1 2001 0.467 0.17 241 0.2 0.1 0.7 0.0 -0.1 0.1 0.0 0.1 0.1 0.8 0.0 0.1 2002 0.508 0.04 247 -0.1 0.3 -0.2 0.0 -0.1 0.0 0.3 0.0 0.1 -0.3 0.3 0.2 2003 0.550 0.04 231 0.5 0.0 -0.2 0.0 0.4 0.1 -0.1 -0.2 -0.1 0.5 0.0 -0.4 2004 0.467 -0.08 247 0.0 0.2 0.0 -0.4 -0.7 -0.2 -0.3 0.4 0.0 0.1 -0.2 0.1 2005 0.600 0.13 255 -0.7 -0.1 0.4 0.5 0.8 0.2 0.4 -0.1 0.2 -0.1 0.0 0.1 2006 0.567 -0.03 246 0.2 0.0 -0.2 0.1 -0.2 0.0 0.0 0.0 -0.2 0.2 0.0 -0.3 2007 0.525 -0.04 233 0.3 0.4 -0.3 -0.3 0.0 0.0 -0.3 0.0 0.1 -0.3 -0.1 0.0 2008 0.517 -0.01 257 -0.1 -0.5 0.3 0.2 0.0 0.0 0.2 0.1 0.0 -0.2 -0.1 0.0 2009 0.633 0.12 260 0.1 0.2 -0.2 -0.2 0.0 0.4 0.2 0.1 -0.1 0.1 0.2 0.6 2010 0.750 0.12 144 1.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 For Country Code 1 From input file ./data/v2.mean.inv.1880.dt
North America in Total?
Even with Canada in the total, North America does not show much going on. In 2008 the whole continent has 0.3 C of total warming over 130 years. By 2009 it’s up to 0.39 C of ‘warming’ including the 92% airports in the USA and all the UHI and bad siting issues. Yeah, 1998 popped up 1.43 C, as it ought to. And that lends some confidence to the method. Similarly 1934 is up 1.175 C (another known hot year) while 1972 is at -0.29 C during the “next ice age” scare. IIRC, it snowed near then in the Central Valley of California. Also about 1989 when we have 0.158 C of “warming” but not from CO2. You don’t get “nothing nothing nothing nothing HOT YEAR nothing COLD YEAR nothing” from CO2. You get that from natural variation. Like 1921 at +1.175 C and without much UHI or airport heating in those numbers…
So we’re dramatically colder than 1921, only somewhat colder than 1934 and about the same as 1881. Even with Canada ;-)
Somehow, I’m not really worried.
North America dT Cumulative CHCN
Produced from input file: ./DTemps/Temps.4 Thermometer Records, Average of Monthly dT/dt, Yearly running total by Year Across Month, with a count of thermometer records in that year ----------------------------------------------------------------------------------- YEAR dT dT/yr Count JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC ----------------------------------------------------------------------------------- 1880 0.000 0.00 272 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1881 0.292 0.29 287 -5.4 -1.1 0.7 -0.3 0.0 -0.7 0.4 0.9 1.6 0.9 2.5 4.0 1882 0.058 -0.23 339 1.8 1.9 0.4 0.0 -2.5 0.0 -0.9 -0.5 -1.1 0.5 0.0 -2.4 1883 -0.550 -0.61 358 -2.2 -2.6 -1.6 0.0 0.2 0.5 0.4 -0.7 -0.7 -1.4 0.5 0.3 1884 -0.400 0.15 400 0.0 0.6 0.6 -0.2 0.8 -0.3 -0.6 0.1 1.0 1.1 -0.2 -1.1 1885 -0.500 -0.10 425 0.4 -1.8 -1.0 0.4 -0.1 -0.3 1.0 -0.2 -0.7 -1.3 0.6 1.8 1886 -0.267 0.23 446 -0.5 2.0 1.0 0.7 0.8 0.1 -0.1 0.8 0.4 0.9 -1.2 -2.1 1887 -0.250 0.02 476 0.6 -0.5 0.1 -0.7 0.9 0.4 0.6 -0.7 -0.4 -1.2 0.3 0.8 1888 -0.400 -0.15 534 -1.0 0.7 -1.2 0.3 -2.1 -0.2 -0.7 0.2 0.0 0.0 0.5 1.7 1889 0.225 0.63 606 3.4 -1.1 2.9 0.8 0.7 -0.5 -0.1 -0.1 0.0 0.0 -0.2 1.7 1890 0.075 -0.15 640 -0.3 1.9 -2.2 -0.7 -0.5 0.9 0.2 -0.3 -0.1 0.5 1.3 -2.5 1891 0.025 -0.05 698 0.8 -0.9 -0.4 0.4 -0.1 -0.7 -1.3 0.4 1.4 0.0 -1.5 1.3 1892 -0.275 -0.30 772 -2.0 1.3 0.6 -1.2 -0.4 0.1 0.7 0.1 -0.6 0.5 0.0 -2.7 1893 -0.525 -0.25 846 -1.2 -2.3 -0.2 -0.2 0.2 0.2 0.4 -0.2 -0.3 -0.3 0.0 0.9 1894 0.233 0.76 886 2.2 -0.2 2.4 1.3 0.9 0.0 0.0 0.4 0.6 0.5 0.1 0.9 1895 -0.325 -0.56 956 -1.5 -1.0 -1.7 0.5 0.0 0.0 -1.0 -0.1 0.4 -1.7 0.0 -0.6 1896 0.150 0.48 986 1.7 3.4 -1.0 0.0 1.2 0.0 1.0 0.2 -1.5 0.5 -0.6 0.8 1897 0.058 -0.09 1032 -0.9 -0.4 0.9 -0.8 -1.4 -0.6 0.1 -0.6 1.8 1.9 0.6 -1.7 1898 0.175 0.12 1058 1.6 0.5 1.6 -0.5 0.0 0.8 -0.2 0.8 -0.2 -1.6 -0.7 -0.7 1899 -0.142 -0.32 1084 -0.9 -4.3 -2.8 0.3 0.0 -0.2 -0.2 -0.2 -0.9 1.3 3.1 1.0 1900 0.583 0.72 1112 1.8 1.9 1.1 0.7 0.6 0.3 0.1 0.8 0.8 1.3 -1.7 1.0 1901 0.167 -0.42 1119 -1.0 -0.3 0.7 -1.2 -0.4 -0.2 1.5 -0.4 -1.0 -0.9 -0.4 -1.4 1902 0.108 -0.06 1145 -0.5 0.8 1.0 0.3 0.6 -0.9 -1.7 -1.0 -0.4 -0.3 2.0 -0.6 1903 -0.258 -0.37 1181 0.1 -0.1 0.3 -0.1 -0.8 -0.7 -0.2 -0.3 0.0 -0.1 -2.3 -0.2 1904 -0.417 -0.16 1214 -1.9 -0.9 -1.4 -0.9 0.0 0.7 -0.3 0.0 0.7 -0.1 1.6 0.6 1905 -0.133 0.28 1233 -0.3 -1.0 2.2 1.0 -0.2 0.4 0.4 0.8 0.5 -0.8 -0.3 0.7 1906 0.250 0.38 1260 3.8 3.2 -4.4 1.1 0.0 -0.1 0.1 0.2 0.5 0.4 -0.6 0.4 1907 -0.200 -0.45 1302 -1.8 0.0 4.1 -3.4 -2.1 -0.9 0.0 -0.9 -1.2 0.0 0.1 0.7 1908 0.375 0.58 1337 1.2 -0.1 -0.6 2.8 1.9 0.6 0.3 0.0 0.8 -0.1 0.8 -0.7 1909 -0.050 -0.42 1369 -0.7 1.1 -1.3 -1.6 -0.6 0.7 -0.5 1.0 -0.7 0.0 0.9 -3.4 1910 0.375 0.42 1384 -0.3 -2.7 4.5 2.4 0.1 -0.5 0.7 -0.9 0.4 1.6 -2.2 2.0 1911 0.350 -0.02 1421 0.5 2.2 -3.0 -1.8 1.7 1.4 -0.3 0.0 0.2 -1.2 -1.5 1.5 1912 -0.408 -0.76 1447 -3.8 -1.4 -3.6 0.6 -0.4 -1.8 -0.2 -0.5 -1.1 0.3 2.6 0.2 1913 0.267 0.67 1484 3.7 -0.5 1.5 0.3 -0.6 0.8 0.4 1.6 0.1 -0.8 1.2 0.4 1914 0.183 -0.08 1514 1.5 -0.6 0.6 -0.5 0.7 0.3 0.4 -0.7 0.2 1.8 -0.9 -3.8 1915 0.233 0.05 1547 -2.2 3.9 -1.2 2.3 -1.3 -1.6 -1.4 -0.7 0.2 -0.2 0.0 2.8 1916 -0.308 -0.54 1575 -0.7 -2.2 0.9 -2.4 0.4 0.0 1.7 1.0 -0.5 -1.4 -1.1 -2.2 1917 -0.867 -0.56 1593 0.1 -1.4 -0.4 -0.9 -2.0 0.2 -0.2 -0.6 0.0 -1.4 1.1 -1.2 1918 0.183 1.05 1624 -2.3 1.8 2.8 0.3 2.7 1.5 -0.9 0.9 -1.0 3.1 -0.7 4.4 1919 0.183 -0.00 1624 4.8 0.1 -1.8 0.9 -0.4 0.1 1.0 -0.3 1.9 -1.5 -1.3 -3.5 1920 -0.058 -0.24 1637 -2.7 0.3 -0.1 -2.3 -0.4 -1.0 -0.8 -0.4 0.0 1.4 0.5 2.6 1921 1.175 1.23 1664 3.0 1.6 2.7 2.5 0.7 1.5 1.2 0.2 0.6 -0.1 0.5 0.4 1922 0.442 -0.73 1682 -2.8 -2.5 -2.4 -0.3 0.6 -0.2 -1.2 0.4 0.1 0.0 0.5 -1.0 1923 0.300 -0.14 1701 2.5 -1.1 -1.6 -0.5 -1.1 -0.6 0.4 -0.7 -0.8 -1.2 0.5 2.5 1924 -0.300 -0.60 1706 -3.0 2.6 0.5 0.0 -0.8 -0.4 -0.9 0.3 -1.3 1.4 -0.7 -4.9 1925 0.517 0.82 1714 0.6 1.2 2.3 2.2 0.9 0.9 0.9 0.0 2.0 -3.4 -0.6 2.8 1926 0.333 -0.18 1735 1.7 0.0 -1.9 -2.3 0.8 -1.1 0.0 0.2 -1.4 2.9 -0.2 -0.9 1927 0.325 -0.01 1735 -0.3 0.0 1.4 0.5 -0.9 -0.3 -0.4 -1.4 0.8 0.7 1.1 -1.3 1928 0.450 0.13 1739 0.6 -1.1 -0.1 -1.5 1.0 -0.4 0.3 1.2 -1.1 -0.5 0.0 3.1 1929 -0.108 -0.56 1754 -3.0 -3.5 0.7 1.6 -1.2 0.7 0.0 0.1 0.4 -0.3 -1.4 -0.8 1930 0.550 0.66 1766 -0.9 5.5 -1.6 1.2 0.5 0.6 0.7 0.5 1.0 -1.0 1.2 0.2 1931 1.433 0.88 1779 4.8 0.0 -0.3 -0.8 0.0 0.8 0.4 -0.4 1.0 2.3 1.2 1.6 1932 0.425 -1.01 1787 -0.4 -1.4 -1.4 0.0 0.6 -0.4 -0.9 0.3 -1.5 -1.9 -2.3 -2.8 1933 0.717 0.29 1795 0.7 -2.5 2.0 -0.5 -0.1 0.8 0.4 -0.3 1.0 0.4 0.7 0.9 1934 1.175 0.46 1806 -0.1 0.9 0.0 1.4 1.7 -0.3 0.6 0.2 -1.5 1.0 1.9 -0.3 1935 0.267 -0.91 1812 -2.5 1.7 0.6 -1.9 -3.2 -1.5 -0.1 0.0 0.2 -1.1 -2.7 -0.4 1936 0.367 0.10 1818 -1.2 -6.3 0.4 0.0 2.9 1.3 0.6 0.9 0.8 0.0 0.2 1.6 1937 0.350 -0.02 1826 0.0 4.2 -2.1 0.5 -0.7 -0.3 -1.0 0.0 -0.3 0.1 0.4 -1.0 1938 1.100 0.75 1834 2.2 1.4 2.9 0.7 -0.8 -0.2 -0.2 -0.3 0.6 1.5 0.0 1.2 1939 0.958 -0.14 1840 1.2 -2.3 -2.0 -0.5 1.1 -0.1 0.2 -0.4 0.0 -1.4 0.7 1.8 1940 0.417 -0.54 1835 -4.6 1.9 0.0 -0.3 -0.7 0.2 -0.2 -0.4 -0.4 0.8 -1.7 -1.1 1941 0.975 0.56 1862 3.3 -0.1 -0.6 1.7 0.8 0.0 0.2 0.0 -0.5 -0.1 1.9 0.1 1942 0.617 -0.36 1891 0.0 -0.8 1.5 0.0 -1.0 -0.2 -0.3 -0.1 -0.3 -0.1 -0.5 -2.5 1943 0.483 -0.13 1906 -1.6 1.9 -2.4 -0.9 -0.3 0.3 0.1 0.5 0.0 -0.3 0.0 1.1 1944 0.708 0.23 1925 2.9 -0.6 0.1 -0.9 1.5 0.0 -0.5 -0.2 0.7 0.4 0.1 -0.8 1945 0.417 -0.29 1979 -1.6 0.2 3.9 0.3 -2.3 -1.3 -0.1 0.0 -0.3 -0.6 -0.7 -1.0 1946 0.858 0.44 1987 0.5 -0.7 0.4 1.5 0.5 0.9 0.2 -0.7 0.0 0.1 0.3 2.3 1947 0.525 -0.33 2014 -0.1 -1.2 -3.7 -1.4 0.1 -0.5 -0.1 1.5 0.4 2.2 -0.9 -0.3 1948 0.308 -0.22 2151 -1.3 -0.4 -0.4 0.5 0.3 0.8 0.0 -0.8 0.0 -2.3 1.4 -0.4 1949 0.575 0.27 2295 0.0 0.3 1.2 0.0 0.5 0.2 0.3 0.1 -1.0 0.5 0.8 0.3 1950 -0.033 -0.61 2320 0.3 0.9 -1.3 -1.8 -0.7 -0.6 -1.2 -1.1 0.0 0.7 -2.3 -0.2 1951 0.017 0.05 2406 0.1 0.2 -0.1 1.0 0.6 -0.4 0.8 0.6 0.1 -1.1 -0.6 -0.6 1952 0.675 0.66 2439 0.6 0.9 0.2 0.8 -0.3 1.5 0.4 0.3 0.7 -0.5 1.6 1.7 1953 1.125 0.45 2464 1.5 0.4 2.0 -1.0 0.0 -0.1 -0.1 0.0 0.0 1.6 1.1 0.0 1954 0.800 -0.33 2474 -2.6 1.6 -1.6 0.9 -0.9 -0.4 0.1 -0.2 0.0 -0.5 0.0 -0.3 1955 0.217 -0.58 2408 0.8 -4.0 -0.2 0.4 1.2 -0.7 0.1 0.8 -0.3 0.0 -3.3 -1.8 1956 0.283 0.07 2416 0.0 0.3 0.0 -1.6 -0.3 0.9 -0.9 -0.9 -0.6 0.2 1.6 2.1 1957 0.508 0.23 2442 -1.7 1.9 1.0 0.9 -0.1 -0.1 0.6 0.0 0.4 -1.3 0.4 0.7 1958 0.450 -0.06 2444 2.8 -2.3 -0.7 0.1 0.8 -0.5 -0.4 0.6 0.1 0.9 0.3 -2.4 1959 0.392 -0.06 2448 -1.9 0.3 0.2 0.0 -0.4 0.7 0.2 0.0 0.0 -0.6 -1.6 2.4 1960 0.283 -0.11 2451 0.8 0.2 -2.5 0.3 -0.3 -0.2 0.0 -0.2 0.4 0.8 1.6 -2.2 1961 0.325 0.04 2465 -0.4 1.4 3.1 -1.4 -0.6 0.2 -0.1 0.2 -0.6 -0.4 -0.6 -0.3 1962 0.300 -0.02 2514 -0.8 -1.4 -1.3 1.0 1.2 -0.4 -0.6 -0.3 -0.3 0.9 0.8 0.9 1963 0.400 0.10 2565 -0.8 -0.1 1.6 0.3 -0.5 0.1 0.6 -0.2 0.7 1.3 0.2 -2.0 1964 0.158 -0.24 2562 2.8 0.2 -2.1 -0.5 0.4 -0.3 0.2 -0.6 -0.9 -2.5 -0.5 0.9 1965 0.075 -0.08 2568 -1.0 -1.0 -0.5 0.2 0.0 -0.4 -0.8 0.3 -0.5 0.2 0.3 2.2 1966 0.033 -0.04 2579 -2.0 0.4 2.5 -0.8 -0.8 0.5 1.0 0.0 1.0 -0.6 -0.6 -1.1 1967 0.167 0.13 2578 3.1 -0.5 -0.5 0.3 -0.7 0.0 -0.9 0.2 0.0 0.4 -0.1 0.3 1968 0.100 -0.07 2582 -1.9 0.4 1.2 0.5 0.4 -0.1 0.1 -0.3 0.1 0.5 0.0 -1.7 1969 0.183 0.08 2591 -0.3 0.7 -2.9 0.5 1.0 -0.2 0.3 0.8 0.0 -1.3 0.2 2.2 1970 0.133 -0.05 2588 -0.6 0.5 0.9 -1.0 0.0 0.6 0.1 0.1 -0.1 0.5 -0.3 -1.3 1971 0.125 -0.01 2493 0.4 -0.6 -0.1 0.0 -0.7 0.2 -0.7 -0.4 0.2 1.1 0.0 0.5 1972 -0.292 -0.42 2490 0.5 -1.2 0.9 -0.4 0.7 -0.7 0.0 -0.1 -0.7 -1.9 -0.6 -1.5 1973 0.550 0.84 2500 1.0 1.0 1.9 0.2 -0.4 0.6 0.6 0.4 0.5 1.9 0.6 1.8 1974 0.233 -0.32 2500 -0.4 0.1 -1.6 0.6 -0.1 -0.5 0.0 -0.8 -1.0 -1.3 0.6 0.6 1975 0.133 -0.10 2488 0.8 -0.4 -1.4 -1.7 1.0 0.1 0.2 0.3 0.2 0.7 0.0 -1.0 1976 0.125 -0.01 2477 -0.9 2.4 1.4 2.3 -0.9 0.0 -0.6 -0.1 0.6 -1.8 -1.5 -1.0 1977 0.633 0.51 2463 -1.8 -0.2 1.2 0.6 1.4 0.5 0.5 0.2 0.4 1.5 1.4 0.4 1978 -0.042 -0.67 2459 0.4 -3.5 -1.8 -1.2 -0.9 -0.3 -0.2 0.1 0.0 -0.1 -0.6 0.0 1979 0.000 0.04 2450 -0.8 -1.9 1.0 -0.4 -0.3 -0.2 0.1 -0.2 0.1 0.6 0.5 2.0 1980 0.458 0.46 2435 2.6 3.5 -1.1 1.0 0.7 0.1 0.6 0.6 -0.1 -0.8 0.2 -1.8 1981 1.033 0.57 2335 1.2 2.3 2.1 0.7 -0.6 0.4 -0.4 -0.1 -0.2 -0.2 1.0 0.7 1982 -0.033 -1.07 2280 -4.1 -2.8 -1.5 -2.7 0.5 -1.2 -0.2 -0.7 -0.1 0.5 -1.8 1.3 1983 0.417 0.45 2259 4.0 2.2 0.6 0.1 -1.3 0.6 0.4 1.7 0.4 0.4 1.2 -4.9 1984 0.408 -0.01 2216 -1.6 0.8 -1.1 0.9 0.5 0.3 -0.4 -0.6 -1.0 -0.1 -1.1 3.3 1985 0.017 -0.39 2173 -0.4 -3.5 1.7 1.1 1.1 -0.7 0.2 -1.2 0.2 -0.1 -1.0 -2.1 1986 0.800 0.78 2159 3.0 1.8 0.6 -0.2 -0.1 0.9 -0.1 0.1 0.2 0.0 0.5 2.7 1987 1.133 0.33 2152 -0.6 1.2 -0.7 0.5 0.6 0.4 0.2 0.2 0.7 -0.7 1.9 0.3 1988 0.642 -0.49 2160 -2.0 -1.9 -0.1 -0.7 -0.4 0.0 0.4 0.9 -0.4 0.0 -0.5 -1.2 1989 0.158 -0.48 2151 2.4 -1.8 -1.1 0.0 -0.6 -0.7 -0.2 -0.8 0.0 0.8 -0.8 -3.0 1990 1.025 0.87 1909 1.5 2.8 1.8 0.0 -0.5 0.6 -0.1 0.4 1.1 -0.1 1.0 1.9 1991 0.933 -0.09 1648 -3.6 1.3 -0.4 0.5 2.0 -0.1 0.3 0.2 -0.9 0.2 -2.4 1.8 1992 0.392 -0.54 1621 1.9 0.0 0.0 -0.8 -1.3 -1.2 -1.3 -1.7 -0.3 -0.5 0.4 -1.7 1993 -0.058 -0.45 1617 -1.3 -4.2 -1.4 -0.8 0.3 0.2 0.8 1.4 -0.4 -0.3 -0.4 0.7 1994 0.567 0.62 1610 -1.4 0.4 1.4 1.4 -0.3 1.5 0.2 -0.3 0.9 0.7 1.8 1.2 1995 0.525 -0.04 1577 2.4 2.1 -0.1 -1.5 -0.6 -1.2 0.2 1.4 -0.3 0.1 -1.3 -1.7 1996 0.025 -0.50 1551 -1.8 -0.4 -2.5 0.2 0.7 0.5 -0.5 -1.1 -0.3 -0.6 -0.9 0.7 1997 0.283 0.26 1518 0.2 0.8 2.7 -1.1 -0.9 -0.5 0.1 -0.3 1.2 0.1 0.8 0.0 1998 1.433 1.15 1519 2.6 1.4 -1.1 1.7 2.2 0.1 0.8 1.1 1.3 0.7 2.1 0.9 1999 1.100 -0.33 1545 -0.9 -0.1 0.1 0.2 -1.3 0.0 -0.1 -0.4 -2.1 -0.6 1.3 -0.1 2000 0.683 -0.42 1523 -0.1 0.2 2.1 -0.1 1.1 0.0 -0.7 0.3 0.2 0.5 -4.3 -4.2 2001 0.942 0.26 1532 -0.6 -2.6 -2.7 0.6 -0.2 0.0 0.3 0.2 0.0 -0.5 4.1 4.5 2002 0.817 -0.13 1510 1.8 0.9 -0.5 0.0 -1.6 0.7 0.8 -0.4 1.0 -0.9 -2.5 -0.8 2003 0.642 -0.17 1495 -1.8 -1.8 1.6 -0.7 0.7 -1.3 -0.4 0.6 -1.1 1.7 0.4 0.0 2004 0.675 0.03 1464 -1.0 0.4 1.7 0.3 0.9 0.0 -0.9 -1.8 0.5 0.0 0.5 -0.2 2005 0.900 0.22 1313 1.4 1.9 -2.2 0.0 -1.4 0.8 1.0 1.5 0.8 0.0 0.1 -1.2 2006 1.125 0.23 1272 3.5 -1.3 0.4 0.1 0.1 0.0 0.0 0.0 -0.2 -0.1 0.0 0.2 2007 0.725 -0.40 235 -1.9 -1.5 0.3 -1.7 -0.2 -0.1 -0.4 0.3 0.8 1.2 0.0 -1.6 2008 0.300 -0.42 247 -0.5 0.5 -1.4 0.0 -0.4 0.0 -0.1 -0.8 -0.4 -1.2 -0.1 -0.7 2009 0.392 0.09 234 -0.5 0.8 0.1 0.0 0.2 -0.1 -0.3 0.0 0.3 -0.6 1.1 0.1 2010 0.475 0.08 175 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 For Country Code 4 From input file ./data/v2.mean.inv.1880.dt
Europe?
Well, Europe is hotter, but it all seems to show up in a ‘bolus’ just about 1990… During the Great Dying of Thermometers. In 1987 we were at -0.3 C, but by 1989 had gotten on track with a +1.25C and held about there to 2005 ( The Year of The Lesser Dying of Thermometers. Though in Europe we see thermometers net added. A Big Dig Here.) at +1.125 C. By 2007 the Dying had gotten all the way to 1.7 C (UHI and airports included) but is now falling back to 1.5 C and dropping.
Interesting stuff, this CO2. Causes step functions in sync with thermometer changes…
European dT Cumulative GHCN
Produced from input file: ./DTemps/Temps.6 Thermometer Records, Average of Monthly dT/dt, Yearly running total by Year Across Month, with a count of thermometer records in that year ----------------------------------------------------------------------------------- YEAR dT dT/yr Count JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC ----------------------------------------------------------------------------------- 1880 0.000 0.00 178 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1881 -0.475 -0.48 189 -0.9 -1.3 -0.9 -1.2 0.2 0.0 0.3 -0.5 -1.3 -0.7 1.1 -0.5 1882 0.350 0.83 196 4.1 1.7 2.4 1.0 0.4 0.0 -0.3 0.1 0.5 1.5 -1.0 -0.5 1883 0.000 -0.35 198 -2.1 -0.1 -4.4 -0.4 0.0 0.9 -0.3 -0.2 0.2 0.3 1.1 0.8 1884 0.142 0.14 203 2.0 0.7 2.9 -0.5 -0.4 -1.2 0.2 0.0 -0.2 -0.1 -2.0 0.3 1885 -0.158 -0.30 206 -2.7 0.3 -0.5 1.1 -0.6 0.9 0.4 -0.9 -0.6 -0.7 0.5 -0.8 1886 -0.025 0.13 210 0.8 -3.3 -1.1 0.2 0.9 -0.5 -0.7 1.0 1.0 1.2 1.4 0.7 1887 -0.175 -0.15 213 0.3 1.5 0.6 -0.8 -0.2 0.4 0.8 -0.1 -0.1 -1.9 -1.1 -1.2 1888 -0.783 -0.61 215 -1.0 -2.2 -1.4 -0.2 0.0 -0.4 -1.8 -0.6 -0.4 1.0 -0.4 0.1 1889 -0.158 0.63 217 0.1 1.2 0.2 0.3 2.0 1.5 1.1 0.5 -0.8 1.2 1.1 -0.9 1890 0.025 0.18 214 2.6 0.4 2.8 0.9 -0.9 -1.5 -0.3 0.9 1.2 -1.3 -0.8 -1.8 1891 -0.175 -0.20 223 -3.7 0.6 -1.0 -1.4 -0.8 0.1 0.5 -1.1 0.0 1.2 -0.5 3.7 1892 -0.308 -0.13 227 1.3 0.2 -1.0 0.5 -0.1 0.2 -0.9 0.8 0.6 -1.2 1.1 -3.1 1893 -0.292 0.02 227 -3.5 -1.4 1.6 0.1 -0.1 0.0 0.8 0.1 -1.1 1.6 -0.4 2.5 1894 0.167 0.46 225 4.1 2.4 0.3 1.5 0.0 -0.6 0.1 -0.3 -1.1 -1.5 1.0 -0.4 1895 -0.108 -0.27 228 -0.1 -4.3 -1.6 -1.2 0.5 0.8 -0.1 0.0 2.3 0.6 0.3 -0.5 1896 -0.100 0.01 233 -0.1 2.8 1.5 -1.1 -0.8 0.4 0.1 -0.4 -0.5 0.6 -2.4 0.0 1897 0.283 0.38 232 0.0 0.6 0.0 1.7 1.1 0.0 0.2 1.1 0.0 -0.8 0.7 0.0 1898 0.375 0.09 225 2.8 -0.1 -2.2 -0.9 -0.5 -1.0 -0.9 -0.1 0.0 0.0 1.9 2.1 1899 0.275 -0.10 229 0.3 0.8 0.6 0.8 -0.4 -0.4 1.0 -0.8 0.0 0.3 0.5 -3.9 1900 0.117 -0.16 233 -1.9 -1.0 -0.7 -1.0 -0.4 0.6 -0.2 0.4 -0.6 0.5 -1.2 3.6 1901 0.317 0.20 215 -0.5 -1.1 1.6 1.4 1.0 1.4 0.7 0.3 0.3 -0.3 -1.2 -1.2 1902 -0.600 -0.92 217 2.1 1.4 -0.3 -1.6 -2.1 -1.7 -2.2 -1.3 -0.9 -1.6 -0.8 -2.0 1903 0.375 0.97 218 -1.2 2.3 1.6 0.6 1.9 0.4 0.6 0.2 0.8 0.5 2.2 1.8 1904 -0.067 -0.44 215 -0.2 -1.4 -2.2 0.2 -0.8 -0.6 0.2 0.2 -0.7 0.9 -1.1 0.2 1905 0.192 0.26 215 -1.3 -0.6 1.1 -0.8 1.0 1.5 0.7 0.3 0.8 -1.6 1.5 0.5 1906 0.433 0.24 215 2.4 -0.2 0.0 1.4 0.8 -0.4 -0.3 -0.5 -0.9 1.3 0.5 -1.2 1907 -0.400 -0.83 213 -2.5 -1.2 -0.8 -1.5 -1.2 -0.5 -1.0 -0.4 0.4 1.4 -2.4 -0.3 1908 -0.342 0.06 206 0.9 1.9 -0.6 -0.2 0.1 0.4 0.5 0.1 0.0 -1.7 -0.7 0.0 1909 -0.192 0.15 204 -0.2 -2.7 0.0 0.4 -1.1 -1.2 -0.6 0.6 1.3 1.8 1.5 2.0 1910 0.467 0.66 206 2.0 3.8 1.7 1.0 1.2 1.3 0.2 -0.5 -1.3 -2.0 0.0 0.5 1911 0.150 -0.32 205 -1.7 -4.0 -1.1 -0.8 0.4 -0.9 0.8 1.6 0.5 0.0 1.7 -0.3 1912 -0.292 -0.44 208 -0.9 1.8 2.4 -0.4 -1.6 1.0 -0.9 -2.1 -1.6 -1.7 -1.7 0.4 1913 0.425 0.72 210 1.6 0.0 -0.1 1.9 0.1 -1.3 -0.2 1.0 1.8 1.8 2.4 -0.4 1914 0.408 -0.02 213 -0.7 3.0 0.0 -0.3 0.5 0.7 1.4 -0.4 -1.0 -0.3 -3.2 0.1 1915 -0.108 -0.52 212 2.2 -1.7 -2.9 -0.7 -0.2 -0.1 -0.5 -0.5 -0.1 -0.4 0.1 -1.4 1916 0.358 0.47 203 0.7 0.3 1.2 0.5 0.0 -0.5 0.0 0.1 -0.3 0.7 2.2 0.7 1917 -0.242 -0.60 203 -3.2 -4.6 -2.2 -1.2 -0.3 2.0 -0.1 1.4 1.8 0.4 0.2 -1.4 1918 0.325 0.57 199 0.9 3.7 2.3 1.6 0.0 -2.3 -0.2 -1.3 -0.2 1.2 -0.9 2.0 1919 -0.233 -0.56 195 0.8 -1.4 -0.9 -0.9 -0.3 1.0 -0.3 0.1 0.7 -1.5 -2.6 -1.4 1920 0.617 0.85 198 0.0 1.3 3.3 2.1 2.6 0.0 1.2 0.6 -0.5 -1.2 0.8 0.0 1921 0.650 0.03 207 1.1 -0.8 -0.2 -0.4 0.3 0.2 -0.3 0.2 -0.8 1.8 -0.7 0.0 1922 0.142 -0.51 209 -2.4 -0.2 -1.0 -1.9 -0.8 0.0 -0.1 -0.6 -0.3 -1.5 1.8 0.9 1923 0.475 0.33 212 2.3 0.0 0.3 -0.8 -0.8 -1.6 0.2 -0.6 1.3 2.8 1.4 -0.5 1924 0.208 -0.27 214 -3.0 -0.8 -2.1 0.4 0.8 1.9 -0.6 0.7 0.8 -0.8 -1.0 0.5 1925 0.675 0.47 217 3.5 4.4 1.1 1.2 0.0 -1.1 1.0 0.4 -1.8 -1.2 -0.8 -1.1 1926 0.425 -0.25 230 -2.2 -1.8 0.1 0.0 -1.0 0.3 -0.9 -0.9 0.6 -0.1 2.7 0.2 1927 0.267 -0.16 229 -0.5 -1.3 1.1 -0.4 -0.3 0.4 1.0 1.7 0.2 0.9 -2.4 -2.3 1928 0.150 -0.12 229 1.2 0.0 -2.6 0.0 0.0 -1.5 -0.5 -1.4 -0.3 -0.6 1.9 2.4 1929 -0.258 -0.41 238 -2.7 -5.7 -0.3 -2.8 1.7 0.6 -0.3 1.5 0.0 1.6 0.0 1.5 1930 0.892 1.15 241 4.4 5.5 3.0 3.7 -0.6 0.7 0.0 0.0 -0.3 -0.7 -0.2 -1.7 1931 0.092 -0.80 225 -2.7 -1.2 -2.0 -1.7 0.7 0.2 1.1 -1.0 -0.7 -0.8 -1.3 -0.2 1932 0.650 0.56 228 2.3 -0.7 -0.8 0.6 -0.7 -0.1 -0.7 1.2 2.0 1.1 0.2 2.3 1933 -0.083 -0.73 229 -3.4 1.7 1.7 -0.3 -1.0 -0.4 0.4 -1.2 -0.7 -0.4 -0.5 -4.7 1934 1.158 1.24 231 2.1 1.3 0.9 1.1 2.0 0.3 0.2 0.3 0.9 0.8 1.4 3.6 1935 0.675 -0.48 217 -1.7 0.9 -0.8 -0.3 -2.3 0.9 -1.3 0.1 -0.6 0.3 -1.3 0.3 1936 0.892 0.22 229 2.8 -2.2 0.8 -0.3 1.0 0.3 1.6 0.2 -0.7 -2.3 0.9 0.5 1937 1.050 0.16 227 -3.0 1.8 -0.1 1.2 0.6 -0.7 -0.8 0.4 2.2 2.2 -0.1 -1.8 1938 1.317 0.27 225 1.6 1.0 1.0 -1.3 -1.1 -0.1 1.0 0.6 -0.2 0.4 1.4 -1.1 1939 0.725 -0.59 226 0.1 0.7 -2.0 0.4 0.2 0.4 -1.1 -0.7 -2.1 -2.6 -1.4 1.0 1940 -0.300 -1.02 236 -5.5 -4.4 -1.3 -0.4 -0.2 -0.4 -0.4 -0.6 0.8 0.3 0.6 -0.8 1941 -0.650 -0.35 245 1.4 2.3 0.4 -0.7 -1.6 -1.0 0.8 -0.7 -0.8 -0.5 -2.6 -1.2 1942 -0.675 -0.02 233 -2.1 -2.6 -2.1 0.0 1.4 0.3 -1.4 0.5 0.8 1.9 0.5 2.5 1943 0.650 1.32 236 2.9 3.7 3.3 1.9 0.7 0.4 0.3 0.3 -0.1 0.4 1.4 0.7 1944 0.658 0.01 247 3.6 -0.1 0.2 -1.1 -0.6 -0.5 -0.1 -0.2 0.4 -0.2 -0.1 -1.2 1945 0.067 -0.59 243 -2.9 -1.0 -0.4 0.0 -0.1 0.2 0.3 0.5 -0.4 -1.2 -0.8 -1.3 1946 0.558 0.49 252 1.3 0.5 0.1 0.8 1.0 1.0 0.2 0.3 1.0 -1.4 0.2 0.9 1947 0.442 -0.12 253 -1.9 -3.7 0.0 0.3 -0.1 0.3 0.1 -0.4 -0.2 1.3 0.8 2.1 1948 0.817 0.38 253 4.1 3.4 0.1 -0.2 1.2 -0.1 -1.4 -0.4 -0.9 0.8 -0.6 -1.5 1949 0.967 0.15 293 0.0 0.3 -0.6 -0.3 -0.2 -0.9 0.5 -0.3 0.7 0.0 1.0 1.6 1950 0.650 -0.32 305 -5.2 0.1 1.5 1.7 -0.2 0.3 -0.2 0.0 -0.1 -0.1 -0.7 -0.9 1951 0.642 -0.01 524 1.9 -0.7 -0.6 -0.3 -0.9 -0.1 0.0 0.7 0.1 -0.7 -0.1 0.6 1952 0.358 -0.28 561 1.0 0.3 -2.0 -0.1 0.1 -0.1 0.5 -0.3 -1.4 1.1 -1.3 -1.2 1953 0.492 0.13 569 -1.1 -1.5 2.0 -0.3 0.7 0.9 0.0 -0.4 0.4 0.7 -0.2 0.4 1954 0.108 -0.38 576 -2.8 -2.9 0.6 -1.9 0.0 0.1 -0.6 -0.2 0.7 -0.2 1.2 1.4 1955 0.258 0.15 588 4.0 4.5 -1.8 0.2 -0.9 -1.5 0.4 0.1 0.0 0.2 -0.8 -2.6 1956 -0.742 -1.00 580 -1.0 -6.3 0.0 0.0 0.4 0.5 -1.1 -0.9 -1.2 -1.2 -1.9 0.7 1957 0.750 1.49 582 0.1 8.5 1.6 1.5 -0.3 0.4 1.4 0.7 0.5 0.7 2.8 0.0 1958 0.292 -0.46 584 0.0 -1.3 -2.2 -1.8 1.5 -1.2 -0.8 0.0 -0.2 0.1 0.2 0.2 1959 0.583 0.29 593 0.7 -1.0 3.7 2.0 -0.8 0.9 1.6 0.2 -0.9 -1.5 -1.1 -0.3 1960 0.617 0.03 598 -0.9 -0.6 -1.8 -0.5 0.3 0.5 -1.3 -0.6 0.3 1.2 1.5 2.3 1961 0.942 0.33 711 0.2 2.2 1.7 1.0 -0.5 0.2 -0.2 0.1 1.0 0.8 -0.4 -2.2 1962 0.333 -0.61 721 1.2 -2.0 -3.0 -0.4 0.0 -1.7 -0.3 0.1 -0.3 -0.4 0.5 -1.0 1963 -0.075 -0.41 748 -4.7 -2.1 -1.0 -0.8 0.9 0.5 1.1 0.2 0.8 -0.2 0.5 -0.1 1964 0.117 0.19 773 1.8 1.0 1.0 0.0 -0.6 1.1 -0.4 -1.1 -0.9 -0.2 -1.3 1.9 1965 -0.192 -0.31 800 1.7 -1.1 0.9 -1.0 -1.0 -0.7 -0.8 0.0 0.2 -0.9 -1.6 0.6 1966 0.708 0.90 807 -0.7 3.4 0.6 1.9 1.2 0.0 0.8 0.9 -0.5 2.2 2.1 -1.1 1967 0.375 -0.33 813 -0.9 -2.0 0.5 -0.8 0.1 -1.0 0.0 0.3 0.7 0.0 0.0 -0.9 1968 0.192 -0.18 810 -0.6 0.7 -0.5 1.1 0.0 1.0 -0.8 -1.0 -0.4 -1.6 -0.4 0.3 1969 -0.250 -0.44 809 0.3 -2.0 -1.8 -1.8 0.0 -0.5 0.0 0.7 0.1 0.4 0.3 -1.0 1970 0.292 0.54 798 1.9 1.5 1.5 1.1 -0.7 0.6 0.7 -0.2 -0.1 -0.8 0.0 1.0 1971 0.292 0.00 773 1.1 0.3 -1.0 -0.6 0.8 -0.9 0.0 0.5 0.0 -0.1 -0.9 0.8 1972 0.200 -0.09 776 -3.9 -0.2 1.6 1.1 -0.8 0.9 0.6 -0.2 -0.6 0.1 0.3 0.0 1973 0.183 -0.02 791 2.1 1.6 -0.3 -1.2 0.6 -0.1 -0.6 -0.3 0.7 0.1 -1.6 -1.2 1974 0.525 0.34 791 0.2 0.4 1.3 -0.3 -1.1 -0.3 -0.5 0.0 0.1 0.4 1.8 2.1 1975 0.700 0.17 809 1.7 -1.5 -0.2 1.7 1.3 0.2 0.9 0.1 1.0 -0.3 -1.1 -1.7 1976 -0.333 -1.03 798 -2.4 -1.9 -2.5 -1.1 -0.8 -0.3 -0.8 -1.3 -2.2 -0.4 1.1 0.2 1977 0.333 0.67 799 0.0 3.8 2.7 -0.1 0.4 0.0 0.0 0.6 0.1 0.2 0.8 -0.5 1978 -0.100 -0.43 803 0.8 -2.1 0.0 -0.6 -0.7 -0.5 -0.3 -0.5 0.1 0.6 -1.5 -0.5 1979 0.392 0.49 781 -1.3 0.1 -0.1 0.0 1.3 1.6 -0.1 0.9 1.0 -0.5 0.7 2.3 1980 -0.125 -0.52 779 -0.5 0.0 -2.1 0.1 -1.8 -0.8 0.5 -0.2 -0.4 0.3 -0.5 -0.8 1981 0.558 0.68 750 2.0 0.6 2.1 0.0 0.5 0.5 0.4 0.4 0.7 1.2 -0.2 0.0 1982 0.342 -0.22 668 -0.9 -1.3 -1.0 0.5 0.6 -1.0 -0.4 -0.2 0.6 -1.1 0.6 1.0 1983 0.650 0.31 656 2.0 0.3 0.5 1.5 0.8 0.0 1.0 -0.1 -0.7 -0.1 -0.6 -0.9 1984 0.375 -0.27 656 0.3 0.5 -0.7 -1.4 -0.3 0.0 -1.1 -0.7 0.0 1.0 0.5 -1.4 1985 -0.233 -0.61 655 -3.9 -3.8 -0.9 0.4 0.4 0.0 -0.2 1.8 -0.5 -1.3 -0.4 1.1 1986 0.200 0.43 651 2.8 1.2 1.3 0.5 -0.8 0.9 0.6 -0.3 -0.2 0.3 0.1 -1.2 1987 -0.300 -0.50 646 -3.1 2.3 -3.1 -1.3 -0.5 -0.4 0.2 -1.1 0.5 -0.2 -0.1 0.8 1988 0.492 0.79 636 4.4 0.4 3.2 0.4 1.2 0.5 0.8 1.2 -0.2 0.0 -2.4 0.0 1989 1.225 0.73 633 -0.2 1.5 3.0 2.1 0.0 0.0 -0.6 0.2 0.2 0.3 2.2 0.1 1990 1.267 0.04 570 -0.5 1.3 -0.2 -1.3 -0.2 -0.1 -0.1 0.0 -0.7 0.6 1.1 0.6 1991 0.683 -0.58 327 0.0 -3.6 -0.8 -0.5 -1.9 -0.2 0.5 0.0 1.0 -0.6 -0.1 -0.8 1992 0.800 0.12 327 -0.2 0.8 -0.6 0.0 1.6 0.6 -0.5 0.7 -0.3 -0.9 -0.1 0.3 1993 0.542 -0.26 298 0.5 -0.4 -0.7 0.4 0.0 -0.2 -0.4 -1.2 -0.7 0.6 -1.9 0.9 1994 1.300 0.76 286 0.7 -0.4 1.4 0.4 -0.4 0.1 2.1 0.9 1.5 0.5 2.3 0.0 1995 1.033 -0.27 249 -0.7 2.8 -0.7 -0.6 0.0 0.5 -0.5 -0.1 -1.1 0.8 -1.8 -1.8 1996 0.550 -0.48 286 -0.6 -2.6 -1.6 -0.1 0.0 0.0 -1.2 -0.2 -0.9 -0.6 1.9 0.1 1997 0.958 0.41 281 -0.4 1.9 1.7 -1.1 0.2 0.0 0.2 0.4 1.2 -0.3 -0.2 1.3 1998 1.075 0.12 275 1.6 0.4 -0.8 1.7 0.1 0.4 0.3 -0.3 0.3 0.5 -1.7 -1.1 1999 1.417 0.34 271 -0.3 -1.5 0.9 0.6 0.0 0.0 0.8 0.0 1.2 0.4 0.9 1.1 2000 1.542 0.12 268 -1.2 1.5 -0.4 0.4 0.6 -0.1 -1.1 0.2 -1.3 0.2 1.9 0.8 2001 1.242 -0.30 274 1.8 -0.9 0.7 -1.0 -0.4 -0.7 1.1 0.4 -0.2 0.9 -1.9 -3.4 2002 1.425 0.18 272 -1.1 2.2 0.5 0.1 0.2 1.0 -0.2 -0.4 0.1 -2.1 1.3 0.6 2003 1.308 -0.12 267 -0.5 -4.3 -1.4 -0.5 0.8 0.7 0.4 1.3 0.3 -0.4 0.4 1.8 2004 1.150 -0.16 270 0.0 1.9 0.4 0.6 -2.0 -1.5 -1.1 -1.2 0.2 1.8 -1.1 0.1 2005 1.125 -0.03 305 1.3 -1.4 -1.2 0.1 0.8 0.3 0.6 -0.5 0.1 -0.1 0.0 -0.3 2006 1.375 0.25 302 -2.6 -0.3 -0.5 0.0 0.1 0.5 1.0 0.2 0.8 0.8 1.1 1.9 2007 1.700 0.33 303 4.0 1.9 3.1 0.6 0.8 0.1 -1.1 0.3 -1.7 -1.0 -1.6 -1.5 2008 1.658 -0.04 334 -0.7 0.6 -0.5 -0.2 -0.4 -0.3 -0.1 0.0 -0.1 0.0 1.2 0.0 2009 1.525 -0.13 329 -1.3 -0.9 -1.0 0.0 0.2 -0.1 0.2 0.0 1.0 -0.1 0.7 -0.3 2010 1.383 -0.14 259 -1.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 For Country Code 6 From input file ./data/v2.mean.inv.1880.dt
I’m going to do Asia on a ‘by country’ or ‘by region’ basis. It’s just so big that trying to figure out what’s going on as a whole chunk is hard to grasp.
For now, I think I like this dT/dt hammer. And not just because it seems to confirm thermometer change as the major issue. I like it mostly because it gives a better view into what is really happening. The other approach (just average all the temperatures) was never intended to inform about temperatures. It was to be a simple benchmark for GIStemp (so you only need to know the ‘shape’ going in and coming out to know the transform and are not trying to imply meaning) that got pressed into duties beyond it’s design point.
This one is ‘a keeper’, IMHO, as it tells you what you really want to know: How are temperatures changing over time for each individual thermometer record compared only to itself?
And the answer is: Not much at all. Unless you change a lot of the instruments around in a block or have a poorly sited one …
UPDATE – added dT/dt category
This is just a note that I’ve added a “dT/dt” category to the blog (over on the right side) where you can find all the various dT/dt reports and postings. This will make it a bit easier to find them and keep them from filling up the AGW, GIStemp, and GHCN / NCDC categories.
Musings from the Chiefio 
Damned time zone differences – this’ll have to wait until tomorrow for me to read it properly. Like what I see on a first pass though ;-)
That’s a whole heap of numbers. Have you plotted those derivatives on a graph? (I think visually)
REPLY[ As I said in the posting, I’m working on the graphs. I thought it best to post what I’d already done rather than hold it up for 2 days while I get graphs done. I’m still learning Open Office to the point where I can go quicker. That, and it’s been 2 days of coding frenzy to reach this point, so a bit of a break is due… Oh, and I’ve still got the data production under linux and the graphing under Windoz on the same hardware, so it’s a bit cumbersome to get data from one to the other. I’m hoping to get it all integrated onto one platform in about a week or 2. Until then, I actualy post the data, then cut / paste from the posting into the OO software. (Yes, I could also write it to a FLASH drive and / or use FTP to/from the Mac and / or … but what’s not possible is making more time in which to do and explore all those options. So for now I’ve got Red Hat on the PC and it does not like NTFS file systems and that’s just the way it’s going to stay. I tried Suse and while the windoz integration was better, getting the FORTRAN compilers and other tools sorted out was a pain. They didn’t have an easy “install everything” option in the package. So the linux is optimized for GIStemp operations, not integration, and OO doesn’t like the libraries I’ve got installed.)
Oh for a nice desk / office, lab, and a “summer intern” or two ;-) But it’s just me, some cast off hardware, and the coffee pot…
So you can cut / past / graph / post link. Or you can wait for me to find time. -E.M.Smith ]
The Google “Chrome” browser puts the far right “DEC” column to the right of the general margins, but the tables render correctly.
In MS-IE7 the tables cut off at October.
ChiefIo this looks like a real monster of a project. You must be really bored. Lots of luck man.
REPLY: [ Not bored. Driven. Somebody has to work this stuff out. -E.M.Smith }
n MS-IE8 go to the page tab, left click to drop the menu, move down to text size and move the cursor over the arrow. The default option is medium sized text, if you set it to smalerl you get the table’s all the way out past Dec (at least on my monitor set to 960×600). For S&G I graphed out the dT and dT/yr numbers for the first three tebles and the linear trend of the dT/yr is flat
Hey, Cheif;
As my south Texas buddy used to say,;
“That’s slicker’n deerguts owna doorknob”
Nice pivot from that ludicrous discussion we were having on Gimptemp. Really nice…
I think you’ve uncovered a new phenom; “TelethermoCOnixion”
It seems like the whole homogenization thing just ranamucka after someone decided it was the only way to clean up the noisy, spotty records. It just stinks on ice when things get sparse in time and/or in space.
Might as well just leave all of the discontinuities in there. Is there any evidence that they are not equally positive as negative? I mean, those graphs of adjustments are purportedly carefully balanced about nearly 0, so,
why bother with them at all?
I really like the idea of just dealing with the data as it is.
The wholesale, ongoing ‘adjustments’ of history is just way too creepy.
Simple is beautiful!
Glad to see what you’ve been so busy with!
RR
Chief –
Question: why not run OO on Linux?
Also you could try gnuplot (http://www.gnuplot.info/) or Grace (http://plasma-gate.weizmann.ac.il/Grace/) on Linux. They are free, offer more control than OO (I think – I haven’t plotted with OO much, but they offer more control than Excel does), and won’t take too long for someone with your skills to learn.
REPLY: [ You labor under the false belief that I did not attempt to install OO on Linux. The issue is not desire. The issue is the libraries and release level of Red Hat that is happy with GIStemp and the level asked for by OO. Yes, all things can be solved. Simply supply labor and money. No problem… but since I’m low on both: you get to wait until I have time.
I know folks are just trying to be helpful, but it’s not helping. Please shift the discussion back to GHCN, dT/dt and climate related stuff.
Yes, eventually I’ll get the newer g95 compiler installed. Maybe even on a newer release of Linux. (sunk about a day into trying to get GIStemp to work on Suse in pursuit of the better NTFS/ Windoz integration) And the newer release of GIStemp from 15 Nov 09. And even the x11 package that OO wants on my Mac that is missing there. And… So it goes.
So things get prioritized. Prioritizing does not mean it can’t be done. Or is hard. Or even ‘has issues’. Prioritizing means showing dT/dt works and there is no warming in the Pacific is MORE IMPORTANT to me than making pretty pictures RIGHT NOW and more important than trying to figure out why Suse hides the Fortran compilers and libraries where you can’t find them or need “the better release pro package” or…
So folks: PLEASE stop asking for graphs. I know you want them. I know I’ll make them IN A COUPLE OF DAYS. And I know that it will be on the ‘rig’ that works right now, not on some future dream platform that will not arrive for 4 weeks…
I’d much rather sink what little time I have right now into discussion of dT/dt and what it does or does not say, and maybe even how to make it work better, than waste time repeating: THERE ARE NO GRAPHS AND WILL BE NONE FOR DAYS.
I’m also hoping that in a couple of days I’ll get a Mac with Linux and OO already running on it. So more time sunk into doing maintenance on what may be a soon obsoleted box is not a very good investment of my time. -E.M.Smith ]
Thanks for posting this.
If you could graph this out, I and a site called Political Math would love to see the visual.
I think you’d agree it would be very interesting, no?
–Wake
Hi Chief,
have you considered letting Google do your chart rendering?
http://code.google.com/apis/chart/
Probably easier than faffing about with spreadsheets.
— Rafe
Looks good so far
just here to poke….poke…
Great stuff!
I just copied your table (Produced from input file: ./DTemps/Temps.6) into an Excel file, added an average, across the the months (added a column for the average) and popped it into a Pivot Table, graphed the Pivot and, “Bingo”, practically a flat line since 1880 with some very interesting peaks (and troughs) along the way. By dragging and dropping the various months (or the average coloumn) I was able to see at a glance that unlike the earth, the trend line was flat.
Nothing scientific about it – I am a fisherman – but have survived in a hard business by knowing how to interpret no frills data.
I now stand by for the flak!
Thanks for a very interesting and informative site.
Exellent first-principles idea. I do a bit of habitat modelling and I’ve never understood how temps could be interpolated across a grid and become data. As Box & Draper (1987) said “Essentially all models are wrong, but some are useful”. You might get a useful model by interpolating the dT/Yr data across the planet.
PS Love the DDT metaphore :)
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Ruhroh :Nice pivot from that ludicrous discussion we were having on Gimptemp. Really nice…
Thanks! I’d been pondering how best to do a baseline choice (thus the benchmarking the baseline posting, were we found that it was sort of ‘all the data’ or nothing…) and how to show temperature change over time in as clean a way as possible.
This pretty much lead me to “What is the simplest and cleanest way to get the delta T per delta time” and that lead to this approach. There are only a couple of parts of it that I’m a bit unsatisfied with, and they are not ‘material’ for large selections of thermometers.
It seems like the whole homogenization thing just ranamucka after someone decided it was the only way to clean up the noisy, spotty records. It just stinks on ice when things get sparse in time and/or in space.
Well, I think that is ‘spot on’. My belief is that folks looked at the actual data. Said “Yuck!” and set out to ‘clean it up into something usable’ with all sorts of “bright ideas” and things that could be published.
Pause while they tossed in the kitchen sink ;-)
Nobody thought to just look at the data and accept them as they are. To ask “What can we find out from the data as they stand?”.
Then they got caught up in a world of their own creation.
<i.
Might as well just leave all of the discontinuities in there. Is there any evidence that they are not equally positive as negative?
Well, on a ‘new record’ you get one more line of ‘zero delta’. So if the thermometer at, oh, SFO were changed to a new record number, you would get a year of zeros. That would slightly decrease the trend (in whatever direction it was going). One hopes that equipment change is a less than every few years thing ;-)
Given that GIStemp tosses out any record shorter than 20 years, I’m satisfied that I get more milage from the available data than they do.
Also, the “start of time” introduces a small bias. If you elected to ‘start time’ in 1720, you would have a couple of thermometers start then (a warm time roughly equal to now) and others slowly added in through the low / frozen of 1816 or “The year without a summer”. If you start time in 1920 you get a warm start for the thermometers that existed then. I think there is less of a ‘benchmark bias’ from having each thermometer begin time when it’s life begins, but by chopping off history at 1880, I slightly bias that set of thermometers to whatever trend was going on in 1880. Warmer than 1820 so a slight hiding of warming trend in a very few thermometers. Not a big deal, but a ‘polish point’. It would be interesting to plot the lifetimes of those thermometers and see how many make it to today, or if they are a very small number so the ‘bias’ in them ends soon. I think 1880 was a relatively neutral time (warmer than 1816, but cooler than 1940 and about 60 years from each.) but with more thermometers than 1816 and fewer than 1940. I may measure this bias at some point just to assure it is small.
The biggest biases that I think I’ve found involve equipment changes. The Great Dying and The Lesser Dying of Thermometers in 1990 and 2006 both have mild step functions connected to them. Some (not yet published) sub set tables by country shows Rural not warming, but Urban warming. (Why that Canadian Rural-Non-Airports that was warming caught my eye… and soaked up ANOTHER day in rechecking all the code to make sure I didn’t have something wrong / buggy ;-) Yes, I really don’t trust code, even my own, and I always assume that anything Really Exciting is probably a bug first and a discovery second …
But the investigation showed that Really Crappy stations with heavy dropouts could cause slightly flaky numbers and especially one that came back after being gone for a few decades. (So I’ve got a ‘dig here’ to figure out how best to handle that. Use the “modification flag” to reset zero? Gap of more than N years of data?) If something is in during HOT PDO phase, then out during cold, then back in for the next HOT or even just replaced by a new station during the cold to hot run, you get double dip on the warming with no cooling offsets… so do we go off to ‘grid box’ land to normalize those double dips to a box with ‘nearby’ thermometers? Or just assume thermometer come and go on a random schedule??… and get ‘creepies’ over The Great Dying and The Lesser Dying… knowing that comings and goings are not random? Or just demand thermometer records be over 60 years duration for policy decisions so they must span a PDO ?
From what I’ve measured already, the longer lived stations dominate the total data volume (about 50% of the data from the top quartile of thermometers, IIRC, while the bottom quartile was something closer to 10%? It’s in the prior work I did.) So we know it’s ‘reasonable’ to depend on the bulk of the data not having that kind of coming and going bias. ( I plan to do a ‘best half’ vs ‘worse half’ rough test on the dT data at some point to measure how valid this assumption is…) But it might be ‘reasonable’ to do something like ‘drop all records shorter than a decade’ or ‘drop all years with more than 50% missing data for a thermometer’. Or even ‘reset zero year after 5 years missing years or greater than 1/2 missing data flags’ But I’m trying to have as absolutely minimum changes to the data used as possible and as few ‘data selection rules’ as possible.
If you change nothing, you need to justify nothing.
And frankly, with ‘all data used unfiltered’ showing just about nothing at all in the way of warming, I’m kind of happy with the result. It seems to me that a 1/2 C UHI and maybe 1/2 C of LIA ending over 130 years pretty much explains everything and it ought to be up to the AGW Advocates to prove “Why not?”
I mean, those graphs of adjustments are purportedly carefully balanced about nearly 0, so, why bother with them at all?
Exactly.
Maybe it’s my computer QA and benchmarking experience. But I always want to start with the raw data or as close to it as possible as my first benchmark run. Then and only then, you know what each subsequent change does and can ask “Was that reasonable?” and “Can I prove justification?” Then you know when something ‘strange’ happens and can question it really closely.
FWIW, in some “selected by urban / rural” and some “Airports vs none” runs I’ve seen patterns that generally indicate UHI and Airport Warmth. I need to polish the things a bit more, but having the same region with Rural temps flat and Urban/Airports hot kind of tells the tale… Then the exceptions, like Saskatchewan, really stand out. And that lead to finding some really dirty data and really dodgy stations and knowing that was an issue to deal with. If I had just done the “fill in and forget” I’d never know there was an issue there…
So in the end, I’m generally happy that no ‘big shoe’ will drop on the methodology and I’m pretty sure the trends (or non-trend ;-) seen here will stand up. But I’m also pretty sure there are some little polish points and that there are some interesting things to be found in the details.
(One that occurred to me today was that the “delta T” data file done the way I’ve done it becomes a simple way to identify which thermometers to investigate. If you know that the dT values in any given month tend to run fractional to small single digits, simply ranking the thermometers by max dT tells you where to investigate first. So that 18C January in that one Canadian thermometer [upon it’s return] becomes a simple way to flag it as a possible “booboo’… Was it different equipment? Different location? Over the Tar Sands In Situ Retorting Operation? [ Think burning tons of natural gas per hour under hundreds of hectares of soil… ] So I’m going to make a ‘probable thermometer quality report’ and see if it has predictive value. If it identifies short lived, sparse data, and dodgy thermometers, or those listed in http://www.surfacestations.org as crap. Or even just says that the BosWash Corridor is packed with people ;-)
But for now, I’m mostly interested in running some individual country reports to see if there is a pattern. What I think I’ve seen so far is that “AGW” happens in places of high economic activity and does not happen in areas of low population / economic density. Basically, “AGW” is probably a result of burning oil and coal, but because the thermometers are placed too close to the burning ;-)
Frankly, with the whole Pacific Region being dead flat and North America being close to it, I think we already can safely stick the “Global” part of AGW in the trash can of history. Basically, the whole southern hemisphere seems to be ‘not warming’ for that matter. If Europe, so stuffed with people, cars, jets, factories, etc. that you can hardly wiggle is 1 C or 2 C warmer than when in poverty, I don’t see that as very important. IIF the same thing happens in China as it industrializes, I’m equally satisfied nothing important happened. Those are regional UHI issues. Not my problem and nothing I do with my fuel consumption will change them one whit.
So I’d rather find out if what we really have is just ARW in the dT/dt data. That’s the way it’s shaping up. And I really don’t care if the Airports of the world are 1/2 C or even 2 C warmer than the nearby grassy suburban park…
FYI
Click to access peterson_first_difference_method.pdf
http://www.ncdc.noaa.gov/oa/climate/research/ghcn/ghcngrid.html
REPLY: [ Fascinating. Looks like I’ve recreated the “First Difference Method” after a sort. Not had time to work out the differences between the two yet, but find it interesting that I observed many of the same properties in it. In a way, it’s kind of nice to have the validation… -E.M.Smith ]
Congratulations.
I’m still in awe of the great thermometer dieoff of 1990 and what it implies for scientists in general. It is one of those defining moments where gut feeling provokes vertigo And nausea at the same time: i put myself in the shoes of the person(s) involved in the act… i would need to be heavily medicated for the rest of my life after that in order to function semi-normally as a prozac zombie.
@Ken Buck: Sent an email.
EM.
This method is known as first differences. It’s Willis’ preferred method for looking at these sorts of things. It has a lot to recommend it.
Let me explain.
Lets take a temperature series that is increasing at a rate of 1C per period.
1,2,3,4,5,6,7,8,9,10
Ok. so you can sample the first differences and say that the
series is increasing at 1C per period.
Now lets introduce an equipment change or a change in latitude. Equipment change first lets say the equip change
is ESTIMATED at .5C Plus or minus of course: we then get this:
1,2,3,4,5,6.5,7.5, 8.5,9.5,10.5
or something like that, you get the idea.
Now, if you calculate first differences you get that 1.5C
when the equipment changed. Well one way to handle that is to “adjust” for the change. In reality what this does is underestimate your uncertainty in the data. Why? because equipment change adjustments are ESTIMATES. we estimate that the change is .5C. there is always some error associated with it. But in the way adjustments are don in current processing these uncertainities are “vanished” they are not carried forward.
First differences can handle this differently. Looking at the metadata you simply dont calculate a first different when you get a change in records.
1,2,3,4,5 first difference = 1,1,1,1
6.5,7.5,8.5,9.5,10.5 first difference = 1,1,1,1
You can just drop 5,6.5 from your calculation.
So instead of 9 first differences of 1, you have 8.
Smaller N, larger uncertainty.
BONUS. you dont have to rely on adjustments like TOBS,
latitude changes, altitude changes, any of that stuff.
You dont have to “model” the changes and adjust for them.
Whenever your metadata changes ( new thermometer, new location, new TOBS ) you just chuck that one month
of data when the change happened.
Also, probably no need to worry about missing months.
No need to create a model to fill in the missing data and then PRETEND that you have a higher N. First differences is great because your uncertainty just is what it is. No need to adjust, just calculate the first differences.
No need to combine stations into long records. none of that crap.
Then … you do an area average of first differences.
What do you have when you are done. a pretty damn good estimate
What you cant handle… gradual change at a location UHI and micro site like stuff.
Yeah, I’d figured out most of those goods / bads about the dT/dt approach. Just didn’t realize I was re-ploughing already ploughed ground.
It’s nice to have the confirmation on what I was thinking. And Im only a little worried that it’s documented in “peer reviewed papers” ;-)
So I’ll have to check the results twice now 8-0
And yes, that ‘errors erased on a reset’ is exactly the property I was looking at when thinking about how to handle “long gaps” and “50% holey data” blocks. There has to be an optimal point between just accepting the crappy patches and tossing out any year with one missing month.
It’s the finding it that will take ‘a bit of a think’…
Right now I’m starting from the idea that the 1/2 point is at most 1/2 way from the optimum (and given that both ends ‘have issues’ [ “keep it all” keeps bogus offsets, “toss any year with a month gone” resets way to many whole years]) it ought to be a good first effort.
So I’ll probably put in a ‘reset on 1/2 months missing in a year’ and see what happens. (Though, really, if a station is reporting every August, but not every January, I really ought not to need to reset the August series with the January… that’s the kind of stuff that needs thinking about… ) I’m also tempted to say that 30 years is one “PDO Trend”, so if you are missing 1/2 of that you’ve lost too much of a single direction of the trend… So a 15 year gap gets a reset on restart? Somehow 5 to 10 is more attractive… but is there any justification for a 5 year reset?
I think I need to “audit the data” and measure just how often and how long there are gaps and holey data blocks. If we’re talking a half dozen stations, feh. If it’s 20%…
So if, say, 80% of the data are contiguous and with only modest holes, can the rest be ignored? Filled in? Or reset of the first difference every N years of gap?
(vjones and friends chose ‘ignore’ if a month missing. GIStemp chose ‘fill in’. I chose “accept and proceed’, but I’m thinking about ‘reset on 5-15 year gaps’. Real choices that explain real differences… )
I think I’ll use a metric of “% data loss / fabrication”.
So at present I lose one ‘month’ of data on the first ‘first difference’ calculation (by definition it becomes zero,so for example, it’s ‘delta to the average of all data’ is lost but would be retained in a ‘use all data as the baseline’ approach). With about 7000 total thermometers, I’ll be losing 7000 x 12 total data items. About 420,000 B out of 45 MB IIRC the data format correctly. About 1%. Not too bad. But what about the other methods?
As more ‘reset rules’ are put in, I will lose a higher percentage fo the data to ‘reset to zero’ actions. Yet even if every record were reset 4 times I’m still only at about 5% ‘loss’. Still quite reasonable.
GIStemp fabricates missing data, but also tosses any record shorter than 20 years, so I can look at before and after and get a ‘percent fabricated’ and “%dropped”. Somehow I need to work out how to handle “%dropped THEN fabricated so how do you know?…”
Vjones drops all years with a missing month, so a count of valid months in “all records with a missing month” gives their percent data loss.
The “goal” would be to find that minium “% data loss / fabrication” that still suppresses bogus values from huge holey data blocks or untenable lengths of missing years (followed by a return of the thermometer). So I’ll need some test cases to see what gets suppressed and how well…
Well, that will be a few days work. So it’s going a bit further down the queue. For now I think I’ll just put in the ‘skip holey data’ code and leave it a parameter. I can run it with a couple of ‘guess values’ like 5 years and 10 years while I build a benchmark and prepare the QA / validation method. (You know, those long hard bits that GIStemp looks to have skipped ;-)
Oh, and on the ‘slow gradual’ stuff: I’m quite happy to have those show up as real results (i.e. I don’t expect nor even want a “zero” temperature change to date, I just want an understood value) that can then be compared with expectations. So if we rise 1 C in “Urban” and 1/2 C in “Rural” I would then just ask: Is 1/2 C reasonable UHI? and is 1/2 C reasonable LIA recovery? If so, AGW is nil or in the error bands.
I hate to see a thinker like your self hamstrung by OS and hardware issues.
Have you considered running the ancient version of GISS in a VMware image? (0r open-source visualisation tool of your choice of course)
And then generating your graphs in R? (as blessed by Steve McK himself). I just noticed that the R-Project now provide very nice packages for OpenSuSe.
REPLY:[ The more new paths to explore, the more new hardware and software to try and configure, the more options put on the table, the longer the project will take.
Think about it… If the “issue” is MY limited time and resources, having MORE things taking my time and resources does not speed things up.
BTW, I’m not “hamstrung”, these are the normal and typical issues anyone in IT support runs into All The Time. I’m simply choosing my priorities and some folks don’t like my choices. I chose not to put my time into installing x11 on my Mac so Opera will run on it. And I’m choosing to leave GIStemp on Red Hat 7.2 where it runs fine and where the g95 compiler is happy. I’m happy with that.
I gave Suse 11 a couple of hours and learned I did not like the issues it has. Yes, they are all surmountable, but I chose to put my time into other things. (Like, oh, that nice lentil curry I worked out instead ;-) That does not mean It was impossible, or even particularly complex. Just less interesting to me and less valuable a place to put my time.
Folks would like me to choose to spend an extra 2 to 4 hours making graphs ( I’m still slow at it so that’s about how long it would take me at this point. In a few months it may be 20 minutes, now it isn’t.) I choose to put that time in to figuring out how to improve what I’m discovering so that when I make some graphs, I’m doing it with the best most informative content that matters the most. And sometimes I choose to step away from playing with software and just go read WUWT for an hour.
Why other folks are not happy with my choices is not a problem I wish to explore…
BTW, the next thing I’m going to explore is a nice little G3 Mac with Linux on it that ought to run the bigendian part of GIStemp just fine (and already has OO running on it). So I’m choosing to take that path rather than work on integrating OO into the RedHat 7.2 that’s presently running. I’m choosing to live with a bit of a hack for a few days (i.e. now) rather than spend my day getting libraries on RH7.2 for OO to make graphs for folks Right Now.
Please, just accept that. -E.M.Smith ]
Not yet having dipped my toe into the blogging waters I’ve no way of posting an image. However, for your edification here’s a simple R script to generate a graph of the Kiribati data. As you can see it makes generating lots of graphs easy.
#
# source(“plot.R”)
# myFile <- "Temps.504.txt"
# pm <- readData(myFile)
# x11()
# plotData(pm, "Kiribati")
# dev.off()
# png(file="Temps.504.png", width=1024, height=600)
# plotData(pm, "Kiribati")
# dev.off()
#
readData <- function(fileHandle)
{
p <- p <- scan (fileHandle,skip=6)
width <- length(p) / 16
pm <- matrix (p, width, 16, byrow = TRUE)
pm
}
plotData <- function(data, strTitle="")
{
labels <- c("dT/dt","dT")
indices <- c(3,2)
colours <- c(2,3)
plot.new()
xr <- c(min(data[,1]), max(data[,1]))
yr <- c(min(min(data[,2]),min(data[,3])), max(max(data[,2]),max(data[,3])))
plot(xr,yr, xlab="Year", ylab="Delta in dec C", type="n")
title(main=strTitle)
idx = 0
for (index in indices)
{
idx = idx +1
legendy = (yr[2]-yr[1])*(0.05*idx) + yr[1]
legendx = (xr[2]-xr[1])*.95 + xr[1]
lines(pm[,1], as.numeric(pm[,index]), col=colours[idx])
text( legendx – (xr[2]-xr[1])*0.03, legendy, labels[idx], pos=2)
lxl = c(legendx – (xr[2]-xr[1])*0.02,legendx + (xr[2]-xr[1])*0.02)
lines(lxl, c(legendy,legendy), col=colours[idx])
}
}
Hi — I wouldn’t worry if you are going over ploughed ground — it seems in this area everytime someone regoes over the same ground they tend to find something different.
I am not sure if it is the same idea as Steve Mosher gives above, but I would have thought the Lazarus thermometers would be best handled by skipping the gap — and then using the first record of the “resurrected” series as the base. So if you have years
1,2,3, 4, 5 and then 15,16 17,
you would go 2-1; 3-2 4-3 5-4 then nothing until 16-15, 17-16
It means you are not assuming that any temperature change between year 5 and 15 is all in year 15 (which is what you currently have) or that the “resurrected” thermometer is the same make and in the same position as it was before.
E.M., great post. I have been toying with a similar approach to the published hadCRUT3 data that I’ve downloaded and converted to MS Excel (TM) files, for the USA.
From the 87 records (one per city) in that USA dataset, only a few show any sign of warming – and some show a distinct cooling. The cooling is quite rapid for 5 cities, Eureka, Los Angeles, San Diego (all California), Washington DC, and Marquette (Michigan).
It will be interesting to compare my results with yours, as the data sets may be somewhat different (or the data may be the same, I’m no longer sure on that point).
I posted the hadCRUT3 results at http://sowellslawblog.blogspot.com/2010/02/usa-cities-hadcrut3-temperatures.html
Roger
E.M., don’t know if this is “helping,” but I copied the Europe data just now off this site, and produced very basic graphs on my site for dT/dt, and Cumulative dT. These are .png files, so you may be able to pull them over onto your site, or I can email them to you, if you like.
http://sowellslawblog.blogspot.com/2010/02/europe-giss-temps-since-1880.html
My thought was, while you do the “deep thinking,” maybe some of the rest of us can do the housekeeping chores. Like making graphs of your hammers’ results. Distributed processing, in a manner of speaking.
Folks, mine are very, very basic graphs, and I’m sure somebody out there has more skill / software and can do what I did, cut and paste the data, and graph it. Only prettier.
Roger
REPLY: [ With the image on a server I can either copy it or simply put a link into the posting. About 10 minutes per graph (yes, it’s about 1 minute to actually do the insert. Then it needs some QA, and titles adjusted, and more QA, and…) and very straight forward. I’ll link them in after I’m done “servicing queues”… Thanks! -E. M. Smith ]
I’ve always snickered when I see someone starting their series in the 1880s. Yeah, temperature measurements were getting closer to worldwide, but 1890s probably add a lot of previously off-limits parts of Africa. More importantly, Krakatoa (1883) should bias temperature readings in the 1880s downward significantly compared to what they would have been in the absence of the volcano.
Based on the impact of Pinatubo, Krakatoa would have probably caused close to half a degree reduction in average global temperatures at the maximum temperature reduction and would have had a clear-cut impact for maybe three years. There probably would be a smaller impact, probably lost in the noise, for longer than that, as ocean temperatures took a bit longer to return to normal.
Does starting in 1890 versus starting in 1880 seem to make much difference?
REPLY:[ If you look at the graph of Africa (h/t Roger), I think you can see the Krakatoa dropout. I’m also pretty sure it’s all gone by 1890 and not much changes. ALL the warming that is visible looks to come in The Great Dying of Thermometers followed by The Lesser Dying in 2006. Nice little hockey sticks. For most of the series, you end up near zero net change in the 1980’s, then the hockey sticking begins… -E.M.Smith ]
I graphed the dT’s for each region, (this includes each region from Chiefio above) and placed them here:
http://sowellslawblog.blogspot.com/2010/02/giss-trends-for-world-regions.html
Roger
EM.
Dont worry about doing an annual.
and dont worry about big gaps.
The data is what the data is..
For a given grid in any given month you might have 10 stations.
Those 10 stations.. lets say all 10 report. You got
10 weighted samples for that grid for that month. You got
an average for the month.
Next month you may have 5 stations. So what. Your options are:
1. Drop the 5 values.
2. Average the 5 values.
3. Do a FANCY infill program like Filnet.
Whats filnet do? Filnet loks at surrounding stations and
estimates the missing value by looking at weighed
correlations. So magically you create 5 N that didnt exist
before. ( SEE BRIGGS) this method of estimation
always has errors, but NOBODY carries them forward.
You got a gap of 15 years. Guess what? that a new station.
Look at all the sstuff hansen tries to do to create long station
records. the reference method. You dont need to do it.
Send Willis a mail. he and I were butting heads about something somewhere and he just said “first differences” and I instantly got it. So drop him a mail.. Biggest issue
I can think of is the error due to spatial coverage changes.
OK, I stuck graphs in (h/t Roger), NOW can you look at what this stuff SAYS and get off the 101 ways to do graphs ? ;-)
It looks to me like there is a whole lot of nothing, with some ocean oscillators showing up (AMO, PDO, etc.).
Then as the thermometer counts go nuts we get the hockey sticking of things.
I’m especially intrigued by the flattening of ranges. In many cases, historic range gets squashed in the present. Very much as though thermometers were being moved from areas of high volatility to areas of moderation.
Is it possible that the averages are dominated by the extreme cases and simply suppressing some occasional extremes bias the average upward? And between moving locations from mountains and inland to near the coast, and from true rural to suburbs and cities with tons of fuel per hour burned; those lows WILL get clipped…
I think I need to run this same code on the T-MAX and T-MIN files and see what changes….
Verity published a similar analysis I did of GHCN back to 1702 on her blog at:
http://diggingintheclay.blogspot.com/2010/01/ghcn-data-analysis-simple-approach.html
I think this method has a lot to commend it.
I’ve since done another analysis of the culling of stations, and there is clear evidence that cooler stations were selected, especially between 1989 and 1990.
E.M., I changed the graph titles to GHCN, and corrected the Pacific chart. — Roger
As always, nice work EM.
Re prodigal returns, I’d treat them as new thermometers, and ignore the step between them, just use the changes from the point it (re)appears. then the question is how long can they be gone for. It seems to me possible that monitoring will be restarting in any number of places given the importance and publicity around the issue, so it could be an on-going problem. Perhaps 1-2 years on the basis that with that kind of delay there may well have been change of staff, equipment, observation regime, or other conditions.
EM,
Kevin has just alerted me to this – he thought you ought to see it:
Hi Chiefio
I’ve now written five detailed articles based on pre 1880 thermometers PLUS utilising the extensive and varied observational records that go with them.
I hesitate to say this, but I have become increasingly suspicious as to why Dr Hansen started GISS at 1880.
Looking at his paper giving the rationale for his selection, a date of around 1920 would surely have been a much more logical time to have started if he wanted to capture a good number of widely dispersed stations in BOTH hemispheres.
Instead he commences- rather illogically- in 1880 which falls between the CRU stall of a relatively small number of stations and the logical 1920 benchmark cited above.
So my suspicions have centred on the GISS figures being started deliberately from 1880 because it was a distinct cold trough-the last burst of the LIA- which would accentuate the temperature rise that came after. Temperatures rising from the depths of the LIA, who would have thought it?
So your approach is absolutely logical but it would have been intriguing to know if we would have a plus or minus T value from 1880 if you had started from 1860 or 1870.
Tonyb
REPLY: [ Easy enough to do. After breakfast and coffee I’ll put that version together too. I just did this way for an easier compare with GIStemp, but have the data for both ways (and the program runs the same on both…) -E.M.Smith ]
While I am somewhat intimidated by the expertise of the previous posters, I will, neverthe less, ask this question:
1. Would a scatter plot of the latitude and longitude of the sensors from the maximum number of sensors to the minimum number of sensors, for a given region, overlayed with the temperature deviation, by year, be a useful graphical comparison?
2. Same as above but with a plot of the altitudes of the sensors plotted for the same time period. This might be better represented with a comparative bar graph showing numvers of sensors located at specific altitude ranges pre and post “disappearing” sensors.
As I read the article, if these plots could be accomplished, it would give lie to the incomprehensible NOAA reply and support your contention that the results of the sensor readings needs to be compared with the geographic distribution held reasonably constant.
To clarify, the average person could more stongly relate to the fact that deleting recordings of sensors higher up and further north raises the overall measurement by default. If this can be presented on a map, they can relate to it even more strongly.
For USA would it be possible to do a comparison run for USHCN v USCHN version 2?
REPLY:[ Yes. I’ll put it on my ‘to-do’ list. I can tell you already, though, that USHCN.v2 is “warmer” from other inspections I’ve done of it. See:
that also includes a link to some other folks looking at it too. -E.M.Smith ]
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Hope you don’t mind, but I took your data and did some graphing and processing on it. Feel free to use the graphs if you find them interesting.
http://strata-sphere.com/blog/index.php/archives/12736
Cheers, AJStrata
REPLY:[ Don’t mind at all. Lets me do more other stuff while folks have something nice to look at…. Interesting analysis you did, BTW. Oh, and the reason to sum the dT/dt is to recover the actual temperature pattern, but with it now offset from zero, so different stations can be averaged for trends. (question in comments at strata-sphere.com ) On thing I’d suggest adding, is to look at the 60 year PDO (and perhaps AMO for Europe) as an explanation of the cold ’60s-70s and warm 30s-40s. There is a repeated pattern of a warm period about every 60 years with cold period offset about 30 from those tops. 1940+60= 2000 right when our ‘Global Warming’ ended and we started falling into our present cold spell. -E.M.Smith ]
Whilst you are doing this it may well be worth while also using the tMax and tMin temperatures seperately rather than than the Arithmetic tMean used normally. I am never sure what the tMean figure is supposed to represent anyway. It bears only a statistical reference to tAverage (a genuine calculation of the average temperature at a point over 24 hours) and that probably cannot be retrospectively calculated from the historic temperature record.
This use of tMin and tMax figures directly will allow effects that are confined to each sub-record to be observed individually rather than combined as at present.
In most cases it would appear that these two temperatures have different characteristics over a year which are lost if using the arithmectic tMean figure.
For instance, if using Oxford UK as an example, the tMin figure shows a distinct phase delay when compared to the tMax figure which is lost altogether if using the tMean.
I wont go so far as to say the tMean is meaningless, but as I have indicated, it would appear to be only statistically linked to any other figures which are using temperature as a proxy for energy transfers.
REPLY: [ In past postings I’ve complained a bit about the difference between Tmin, Tmax, and Tmean and I’m pretty sure it matters. I’ve seen patterns that point me in that direction. I have a related idea kicking around in my head right now that I’ll be posting about soon ;-) -E.M.Smith ]
Please note that the ‘First difference method’ as previously described by Peterson at al. at http://go2.wordpress.com/?id=725X1342&site=chiefio.wordpress.com&url=http%3A%2F%2Fhomepage.mac.com%2Fwilliseschenbach%2F.Public%2Fpeterson_first_difference_method.pdf differs from your method in that uses values that are a year apart whereas your method uses month by month.
Thus Peterson uses Jan 00 as diferenced to Jan 01 whereas yours is Jan 00 versas Feb 00.
I am not sure how this changes the underlying statistics though.
REPLY: [ Nope. I’m comparing Jan 00 to Jan 01 as well. What is different is that I don’t (yet) reset on a missing data item. The Peterson method does a new zero if a month is missing. I just carry forward until the data returns. -E. M. Smith ]
Hi all…a bit of light relief….forget the Bolivia Hot Spot. Its not a march of thermometers at all, it really is down to a localised massive CO2 release…and I can prove it.
You see….messers Clarkson, Hammond and May recently bought 3 beat up old 4WD vehicles and drove over Bolivia. As I’m sure you will know, this was an enviromentally polluting act of such massive proportions that it showed up straight away on GISS a a large red spot.
Cause and effect.
I thank you…
Surely the problem with sub-sampling the data at 1/12 frequency is that, if you allow any ‘breathing’ of the period due to low frequency components or phase changes then, given the large yearly range, those low frequency components or phase changes will show up as multiplied temperature drifts in the output.
Your method/Peterson at al. can be easily extended to prevent this by using Jan 00 to Feb 00,etc. deltas (i.e the original sampling frequency) instead. You still capture the full information but without the sub-sampling problems that may otherwise occur.
This can be extended right the way down to daily (or even hourly) deltas if more precision is available/required.
I’ve lost track of the ‘decimation’ process.
How do we get from hourly measurements to Daily Tmin Tmax?
And then, how do we get from Daily Tmin Tmax to Monthlies?
Is there a reference paper on the validity of representing all of the temperature history by a Monthly Tmean calculated in this fashion? (or would a direct mean of the hourly readings give a number that is lower in winter and higher in summer?)
Would the prevailing RH (presumably seasonally different) make a difference?
This is possibly a minor point, but I assume it was originally done this way due to manual processes and limited computational resources.
I realize it will probably all ‘average out’ in the big picture, but we’re already dealing with noise of similar magnitude to the signal, and vigilance is required to avoid converting noise into signal in the sampling process…
@richard, I don’t think the period ‘breathes’ so much as hiccups. I think it is more constant frequency, but missing samples. As I understand the goal, fidelity to ‘phase changes, etc.’ is less the goal than accurate compilation of accumulated Delta Temp.
RR
GREAT HAMMER, EM Smith! If I had a hammer, I’d hammer out “freedom” for all thermometers, “justice” for both rual/ urban and high/low altitude, and “love” for accurate collection and reporting of data plus the scientific method.
E M Smith – thanks – I am interested in the magnitude of the difference in trend over the last century between the 2 versions of USHCN. In particular prior to 2007. Your program would help to give an indication.
EM
check diurnal range.
TMAX-TMIN
It’s pretty well established that (tmax=tmin)/2 is an unbiased estimator of the integrated function..(historically at least ) that is trends will be preserved. However it always helps to check. I think you may find research on global warming and patterns in diurnal range ( i recall reading something)
UHI signals show up in tmin, mostly
EMS
Dont know if you spotted this in the BBC interview with Phil Jones
A – Do you agree that according to the global temperature record used by the IPCC, the rates of global warming from 1860-1880, 1910-1940 and 1975-1998 were identical?
This directly relates to my comment above (12.04 11 Feb) where I am suspicious of Dr Hansens reasons for starting his own figures at a trough of the LIA rather than at the peak immediately before or after. Have you had a chance to have a look yet?
Best wishes
Tonyb
@Tonyb : See the “benchmarking the baseline” thread. Basically, it looks to me like shifting the baseline period would eliminate about 1/2 the perceived ‘warming’ (though any given rate would stay the same, the 60-70s cold period would show as cold rather than ‘normal’… )
And yes, the ‘warming’ looks to me to be perfectly normal just like the other times we’ve had change. If anything, it seems to be less than what the ‘old timers’ in my home town described happening back in the ’30s (when as a small child I asked them about their lives and history…)
@Ruhroh: As far as I know, we don’t do hourly measurements. There is a daily MIN, MAX and TOBS. The mean is calculated from the MIN and MAX readings. (I used to have a min / max thermometer on my patio. They were common in farm country to tell you if it was getting close to a lower bound at night or upper bound in the daytime. So if your ‘min’ started saying 35, 34, … you would prepare the frost treatments for the crops.)
The daily MIN and MAX are used to make a proxy for the daily mean via (MAX – MIN) / 2 and the assumption that the daily cycle is close to a sin wave (rather than triangle or fish scale or even step function like a thermometer in the bottom of a canyon… UUUUU or nnnnn shaped days are assumed to just not happen. And when Dallas has a Canada Express dump on your head and it does a step function down 40 F in an hour and then just lays there for 2 days, that doesn’t happen either ;-) So L____ doesn’t happen in theory… Then this min-max average is averaged for those days of the month that have data and THAT is the proxy for the ‘monthly mean’ that may also be shaped differently…
But everyone just says it’s a good enough approximation…
Cheif;
Yes, that is a good exposition of the rationale for the decimation process.
I had the idea that it is one of those long-held assumptions that has not been rigorously examined recently.
But I was thinking that I have seen hourly data from at least airports, and it would seem to be a relatively (mathematically) straightforward exercise to re-examine the validity of the always-taken shortcuts you describe.
The national agencies are unlikely to do an analysis that would undercut their perceived credibility, especially if there is no ready remedy to the issue.
Of course, talk is cheap and blog text is even cheaper.
My unstated goal was to offer an ‘interesting’ problem that does not involved decryption and reanimation of arcane brittle code, probably more to the Database wizards than ‘yet another timesink’ for El Jefe. You’re obviously well equipped to find those for yourself.
Still working to get the washer fixed for my valentine;
rather an all-or-none endeavor at this point…
RR
The automatedd systems airports use and the newer MMTS systems do take hourly readings. You can see this by looking at the data from the CO-OP network that has them.
At this link you will see a tab for the “climate” for the town of Berlin near where I live. What it shows is the 2009 data from the Snow Hill CO-OP station and you will see that they have not only the average of Tmax and Tmin but also the median temp.
http://www.weather-forecasts.com/cityClimate/United+States/Maryland/Berlin
The reason they do Tmax/Tmin goes back to the old days when the Dept of Agriculture ran the Weather Service since they were concerned with weather not Climate and Tmax/Tmin was what farmers needed. That kept up even into modern times when NOAA took over the NWS.
Thanks boballab;
So, there will be a lot of hourly data sets if they come from the MMTS systems.
Am not sure where I found the raw hourly readings previously.
That would be interesting to see if they report a true ‘arithmetic mean’ of all temperatures or they stuck with the traditional midpoint between Tmin and Tmax.
At least there would seem to be a vast amount of data (albeit scattered?) to examine the validity of the mid-point assumption as ‘Taverage’.
I am hardpressed to explain how this would not have a strong seasonal component of error, given the variation of sunlit hours.
Have I merely overlooked the beer-reef-ewed glitch wherein this foundational assumption {Tave=(Tmax+Tmin)/2} was validated?
If your use of the term “median temp” was not inadvertant, I apparently missed your point.
Also, (OT?) what is the rationale for the ‘TOBS’ adjustment if they have hourly readings around the clock? I’m aware of ‘peak-detecting’ LIG thermometers, but ‘minimum-detecting’ is less obvious to me.
Spuriously,
RR
They computed the median temp from the hourly readings at the station here is what they got for Snow Hill :
Tmean : 57.2 F (which is (Tmax+Tmin)/2)
Tmedian : 56.9 F
If you go back to the real old paper records you will find that they did two different methods. One was to take 3 readings during the day and compute a mean and the other is the one we know of Tmax/Tmin here is a link to an old paper copy from the 1890’s with the instructions:
http://tiny.cc/Xi9wP
Looking at that set of instructions (http://tiny.cc/Xi9wP) then, I would doubt the ability of anyone to compute a tMean that get close to tAverage for almost any period in anything other than a statistical relationship.
If using the two reading tMax and tMin, then tMean records (at best) the afternoon mean temperature. It fails to capture in any way the morning tMean value. The three readings method, at morning, noon(ish) and evening almost certainly fails to capture the tMin value for the day (though it may be close to the tMax value. Again the calculation for tMean is only going to bare some (possibly close, maybe wide) statistical relationship to tAverage.
More modern instruments may allow a much more accurate figure, but that is not available for most the records in the past.
I think that changeing to using a solution tDiff figure for all readings will help to remove at least some of these potential problems.
If the temperature is written down in observational order (thus tMax preceeds tMin in the sequence) then the tDiff figure can be calculated for each step and accumulated as had been suggested previously.
This can also be done for the three readings method, again in observational order.
Now we can calculate tAWG (a delta requirement) for any station using only delta figures. It has the significant advatage that, as it only uses addition and subraction, then any computational errors due to truncation or rounding are kept to the minimum.
This has the additional advantage that adding new readings can be one with very little effort (as the accumulated tDiff is available.
An online, up to date, tAWG on a daily basis, if required, with a very low computational overhead!
It also means that absolute accuracy is less important than lineafality and repeatabilty which are likely to be of higher accuracy.
All we are interested in, after all, is the change over time. We can relate that sequence to absolute tempertures, if we need to, by choosing the most accurate record available, rather than the first.
After thinking about it more closely (I always work better thinking rather than writing), then it becomes obvious that the required tAverage for a period is the tMedian of all of the recorded values during that period.
This can be used for both tMin, tMax records, the pseudo 4 records a day USA variation or any tMean figures that are calculated from the above. In all cases taking the tMedian of all of the figures in a period gives the correct tAverage of the waveform over that period.
The tMean figures for any period only works correctly for symmetrical waveshapes or cases where we only n= 2 values (such as the daily tMin, tMax), whereas the tMedian works correctly for both symmetrical and asymmetrical waveshapes and for all values of n.
Thus it is possible to derive the correct tAverage from a yearly period by taking the median of all of the monthly tMin, tMax records for that year. This can also be done by taking the median of all of the monthly, daily or even hourly tMean figures depending on what accuracy is available. In any case, which ever method is used, the tAverage answer will be the same.
In order to make the output figures relative now rather than trying to do this at a later step, the central point of the output can be set to the median of all of the values over the station record in question (quantised to a yearly aligned sub-set if necessary).
Further smoothed outputs are available by taking 2, 5, 10 or 20 year periods and examining them using the same rule, if required. More detailed views at 3 or 6 month periods are also available if those are of interest.
This method should allow a wide range of stations record sets to be used I expect without compromising accuracy.
OK, I’ve spent a goodly chunk of the weekend thinking about this and I’ve come to a couple of conclusions:
1) My dT/dt method has significant differences from the “First Differences” method.
2) I like mine better.
3) It is important NOT to reset the first value on a gap.
4) Over long periods of time, station selection CAN introduce bias into any of the computational systems (something for another day or perhaps a published paper).
5) Using the average of all data (for a thermometer) vs the first record as the ‘baseline’ has a slight advantage that is probably not worth the effort, but I’m going to try it some time anyway.
OK, on “1”: The “first difference” method will reset on any missing datum. So if I have Jan 1900, then 1901, gap, 1903, 1904 with 0.1 C / year increases, the FD would be 0.1 + 0.1 or 0.2 over 4 years for a 1/2 C / year. I would get 0.1 +0.2, +0.1 = 0.4 / 4 or a more accurate 0.1 C / year. That is, the FD method drops data on gaps and there are lots of gaps. Also the FD method allows an accidental cherry pick or bias via gaps.
Basically, that “gap” of 1902 causes a drop out for FD but causes a “catch up” for me. For a smooth trend, my method will be superior. For a jittery period with equipment changes FD will lose some data while I’ll report a jitter and report the equipment changes as valid data change. For very long gaps, the dT/dt avoids a very critical type of error. In particular, FD lets you have a “cherry pick” of two PDO hot phases without the corresponding “PDO cold” phase offset via having the thermometer ‘drop out’ during the cold phase. You would get all the plus, plus, plus of a hot phase, then a ‘reset’ on the gap, then plus, plus, plus for FD. For dT/dt you get the plus plus plusses. BUT, on that ‘return of the prodigal thermometer’, you would get a LARGE “gap down” from the top of one PDO hot phase to the bottom of the missing cold / start of the new hot PDO phase. Yes, you get an ‘odd blip’ in that one year, but it’s telling you something you really want to know…
2 comes directly from the effects of #1 ;-)
3 comes directly from the analysis of the effects of #1.
4 If you have one station in during the hot PDO phase, then out, then a different station IN during the next PDO hot, you can ‘stack’ two hot runs without an intervening cold phase. Further, by using “high beta” (high volatility) stations in the hot phase, and low beta stations in the cold phase, you can have a ‘representative station’ in a box for that time period, yet get net heating from an un-changing climate. Oh, and there are areas that warm during a warm PDO and areas that cool during a warm PDO, so by using ‘tricks’ like “fill in” from a few thousand kilometers away you can fine tune this error…
5 The use of an average of all data for the baseline vs the dT/dt method does not change the ‘shape’ of the “dT” at each step… with one exception. The very first data item is assumed zero ‘dT’ in the “dT/dt” system. If you measure IT against the aTemp, then you can preserve that first data item AND show that the ‘entry year’ is either warmer or colder than the average for that thermometer. In this way, an ‘accidental cherry pick’ of a cold first year will not show up as 0 +2 +2 but would instead show up as -4 +2 +2 (you would still get the +4 trend for those 2 and 3 years, but would know it was starting from “in a hole”). To the extent you have confidence that 1880 is a ‘sound normal year’ you don’t need to do this. To the extent you would like to have any year chosen with minimal ‘accidental start cherry pick’ impact, then I would suggest adding a step of “average all data for a thermometer record” and “time0 dT is time0 T- aveT. and proceed from there.
Finally, I’ve also done a bit of looking at “extreme data” from the dT file. One thing I’ve learned is that there are often 2 or 3 or sometimes even 4 records for the same place with different “Mod Flags”. (the temperatures I’ve observed all were matches, so the mod flags didn’t change much). BUT, this implies that it’s very important to suppress multiple identical records from influencing the average dT. Basically, you don’t want 4 votes for +20.2 C at one Canadian or Siberian location to bias your result for a country where you might only be averaging 40 thermometers total (as in Canada). So ‘duplicate suppression’ needs to be added and is almost certainly overstating the recent warming trends at high latitudes in dT/dt. (we’ve done a lot of changing from liquid in glass to various electronics lately so many stations have duplicate mod flags recently…)
In conclusion:
I’ll be enhancing my dT/dt method with suppression of duplicates and with a test of “averageT as first record” to see what impact they have. I’ll also be looking at the impact of starting ‘gaps’ with a new T(n) vs aveT. I will also be keeping the ‘gaps’ in as a way to suppress any stacking of hot PDO phases and similar effects. Finally, I’ll be trying a few tests with ‘dropping shorter than 10 or 20 year records’ to see if that changes anything, especially the recent ‘hockey stick’ rises in some places.
How do you combine series in your dT/dt method?
Simple averaging would appear to introduce an error due to the different offsets.
For example, if you have two series, one years 1 to 10, and the second 6 to 10 and both show 0.1C/year rise. At year 6 the delta would be 0.6C from the starting point, or 0.1C above the mean of the first series.
But when the second series comes in at zero (as the first year) or -0.15C below the average for that series it will step the average of the two series down.
I’m sorry if I’m being stupid here and missing the obvious…
Andrew Chantrill … missing the obvious
Well, it’s not obvious. In fact, much of the discussion above is about various pimples and warts on the process of ‘combining series’.
For one thing, it depends on “what is A series?”. You get to choose… Is A series one thermometer at one location with a single “modification history” flag? then each “sum” is based on a 12 digit station ID. If you wish to ignore “mod flags” then you use an 11 digit station ID. If you use 11, then do you just ‘add them together’ giving extra ‘weight’ to any location with multiple “mod flags”? As I found was happening in some cases in Canada where stations with an extreme +20 C change had 4 station records (IIRC) with identical data but different ‘modification history’ flags. Probably not (since that would duplicate the 12 digit StationID result). So here you had Canada with about 45 stations reporting in total, yet this one location got 4 ‘votes’ for an extreme level of change…
Thinking through that: I now have an (as yet not implemented) statement of direction that I need to average ‘mod flags’ together for “a series” prior to averaging that ‘location’ into the whole study area.
Further, does a “gap” in time mean a new “series”? The First Difference method says “yes, on any single data item loss”. So they will simply lose the datum following any drop. A trend of 0.2 / year C would become ZERO if every other record were missing ( the missing one becomes zero AND the one following it would become zero as the FD start was reset). I’ve chosen to say that the ‘gap matters’ so I would take a series of 0.4 C jumps in every other year, but still average to 0.2 C / year. At present, I only drop the very first value (but have proposed using the “average of all data for that thermometer” to the first record, so would not drop any).
You could also choose to ‘amortize’ long gaps over the whole gap. I lump it all into the year where it shows up.
Also, your example seems to describe two DIFFERENT thermometers. If that’s the case:
So in your example, AT PRESENT I would get:
0, 0.1, 0.1, 0.1, ( for a total of 9 samples of 0.1) then:
The second thermometer would be saying nothing for the first 5 years (as there are no data), then year 6 would be zero (due to the initialize as temp delta in start year is zero..) Then 4 years of 0.1 deltas.
During the average calculation, I add those two series together and get:
0, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2. 0.2, 0.2
but then divide each cell by the number of valid datums that make that total (i.e. compute the average) which ought to yield:
0, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
As there were 2 valid thermometers contributing data in years 7, 8, 9, and 10.
So you only ‘take an error’ in the very first year at zero delta. Thus my interest in using the ‘average of all data’ to start the series offset (in this case, it would become T + ( 10 * 0.1 / 10) or T + 0.1 that would then be subtracted from T giving a 0.1 anomaly (and the whole series would become 0.1 C / year – our presumed actual rate of change). A bit simplified as I’d actually use summation of Tn / count of Tn that ought to give a roughly To – T.10 – 0.1 presuming I can get all the code done well… Oh, and year 6 would have 2 thermometer values with an average of 0.2 then divided by 2 giving 0.1 as the ‘delta’. In this case it matches the present method, but you could have a case where that first value was not exactly the same offset as the average and could get a minor jitter in year 6.
Got it?
Yeah, so much for “obvious”…
THEN you get to start asking “Ought you be combining things across grid cells by simple averaging of these dT values?” Or should you do something more ‘complex’?…
Is it more, or less, accurate to simply average the “deltas” for somewhere like SFO that rarely changes much and Death Valley that varies widely? If you have a large enough sample is the answer (to ‘should I average?’) different?
And here I am trying to do this in the most direct, obvious, and provable way possible… thus part of my skepticism about the notion we know what the temperature change has been down to the 1/100 C and even the 1/10 C place…
Thank you for your comprehensive reply; it’s starting to make sense…
In my FD analyses I have used the 12-digit ID, so may have included some duplicated data. On the other hand once one starts removing data sets one might introduce a different error…
I do introduce one unavoidable error in order to get the data manageable – I calculate annual station averages and ignore any years that do not have 12 months of data.
When I started looking at the data I assumed if one looked at the distribution of all temperature data (regardless of year or station) it would approximate to a normal- or skewed-normal curve, but it doesn’t; it has three clear sharp peaks.
I am particularly interested in the distribution of the added and dropped stations and the impact this may have on the robustness of any trends.
Following your comments yesterday, I thought it would be interesting to see what difference there was between defining stations as 11- or 12-digit identifiers.
In the 11-digit sets I used a simple average to combine the data for each station.
The delta between the 11- and 12-digit First Difference analyses is curious and shows a widening of the difference from about 1955.
If you’re interested, I’ve put a graph at:
http://public.me.com/achantrill
I’m struggling to work out what this tells me, but it seems to suggest that those with the most duplicates are the ones with the steepest warming.
In many cases the ‘duplicates’ get a different mod flag due to equipment changes. IIRC, there were some “issues” with the electronic thermometers having a warming bias when first introduced (and some questions still…)
Then there are cases like Death Valley where they have added a thermometer in a carefully chosen hotter spot than the old one…
Finally, I think airports got a lot of the first update to newer electronic thermometers (AWOS? something like that) so any Airport Heat Island effect will show up preferentially in the newer equipment (so more and newer mod flags).
I’m sure there’s more…
I got to say, you’re doing great work. It would seem a few dollars shoved your way, out of the wasted $75 billion promulgating the hoax could be well spend in just doing the numbers.
Very very interesting.
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